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class="control is-expanded"> <label for="query" class="hidden-label">Search term or terms</label> <input class="input is-medium" id="query" name="query" placeholder="Search term..." type="text" value="Lin, W"> </div> <div class="select control is-medium"> <label class="is-hidden" for="searchtype">Field</label> <select class="is-medium" id="searchtype" name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author 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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Lin, W"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.16235">arXiv:2412.16235</a> <span> [<a href="https://arxiv.org/pdf/2412.16235">pdf</a>, <a href="https://arxiv.org/format/2412.16235">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mathematical Physics">math-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Bian%2C+S">Shirui Bian</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+Z">Zezhou Wang</a>, <a href="/search/q-bio?searchtype=author&query=Leng%2C+S">Siyang Leng</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a>, <a href="/search/q-bio?searchtype=author&query=Shi%2C+J">Jifan Shi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.16235v1-abstract-short" style="display: inline;"> Early-warning signals of delicate design are always used to predict critical transitions in complex systems, which makes it possible to render the systems far away from the catastrophic state by introducing timely interventions. Traditional signals including the dynamical network biomarker (DNB), based on statistical properties such as variance and autocorrelation of nodal dynamics, overlook direc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.16235v1-abstract-full').style.display = 'inline'; document.getElementById('2412.16235v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.16235v1-abstract-full" style="display: none;"> Early-warning signals of delicate design are always used to predict critical transitions in complex systems, which makes it possible to render the systems far away from the catastrophic state by introducing timely interventions. Traditional signals including the dynamical network biomarker (DNB), based on statistical properties such as variance and autocorrelation of nodal dynamics, overlook directional interactions and thus have limitations in capturing underlying mechanisms and simultaneously sustaining robustness against noise perturbations. This paper therefore introduces a framework of causal network markers (CNMs) by incorporating causality indicators, which reflect the directional influence between variables. Actually, to detect and identify the tipping points ahead of critical transition, two markers are designed: CNM-GC for linear causality and CNM-TE for non-linear causality, as well as a functional representation of different causality indicators and a clustering technique to verify the system's dominant group. Through demonstrations using benchmark models and real-world datasets of epileptic seizure, the framework of CNMs shows higher predictive power and accuracy than the traditional DNB indicator. It is believed that, due to the versatility and scalability, the CNMs are suitable for comprehensively evaluating the systems. The most possible direction for application includes the identification of tipping points in clinical disease. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.16235v1-abstract-full').style.display = 'none'; document.getElementById('2412.16235v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.02698">arXiv:2409.02698</a> <span> [<a href="https://arxiv.org/pdf/2409.02698">pdf</a>, <a href="https://arxiv.org/format/2409.02698">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> </div> </div> <p class="title is-5 mathjax"> Exact first passage time distribution for second-order reactions in chemical networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Rao%2C+C">Changqian Rao</a>, <a href="/search/q-bio?searchtype=author&query=Waxman%2C+D">David Waxman</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a>, <a href="/search/q-bio?searchtype=author&query=Song%2C+Z">Zhuoyi Song</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.02698v1-abstract-short" style="display: inline;"> The first passage time (FPT) is a generic measure that quantifies when a random quantity reaches a specific state. We consider the FTP distribution in nonlinear stochastic biochemical networks, where obtaining exact solutions of the distribution is a challenging problem. Even simple two-particle collisions cause strong nonlinearities that hinder the theoretical determination of the full FPT distri… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.02698v1-abstract-full').style.display = 'inline'; document.getElementById('2409.02698v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.02698v1-abstract-full" style="display: none;"> The first passage time (FPT) is a generic measure that quantifies when a random quantity reaches a specific state. We consider the FTP distribution in nonlinear stochastic biochemical networks, where obtaining exact solutions of the distribution is a challenging problem. Even simple two-particle collisions cause strong nonlinearities that hinder the theoretical determination of the full FPT distribution. Previous research has either focused on analyzing the mean FPT, which provides limited information about a system, or has considered time-consuming stochastic simulations that do not clearly expose causal relationships between parameters and the system's dynamics. This paper presents the first exact theoretical solution of the full FPT distribution in a broad class of chemical reaction networks involving $A + B \rightarrow C$ type of second-order reactions. Our exact theoretical method outperforms stochastic simulations, in terms of computational efficiency, and deviates from approximate analytical solutions. Given the prevalence of bimolecular reactions in biochemical systems, our approach has the potential to enhance the understanding of real-world biochemical processes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.02698v1-abstract-full').style.display = 'none'; document.getElementById('2409.02698v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 pages, 4 figures, journal article</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.01621">arXiv:2407.01621</a> <span> [<a href="https://arxiv.org/pdf/2407.01621">pdf</a>, <a href="https://arxiv.org/format/2407.01621">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Deciphering interventional dynamical causality from non-intervention systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Shi%2C+J">Jifan Shi</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+Y">Yang Li</a>, <a href="/search/q-bio?searchtype=author&query=Zhao%2C+J">Juan Zhao</a>, <a href="/search/q-bio?searchtype=author&query=Leng%2C+S">Siyang Leng</a>, <a href="/search/q-bio?searchtype=author&query=Aihara%2C+K">Kazuyuki Aihara</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+L">Luonan Chen</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.01621v1-abstract-short" style="display: inline;"> Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much attention but remain extremely challenging. To address this challenge, we propose a framework named Interventional Dynamical Causality (IntDC) for such non-intervention systems, along with its computational crite… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.01621v1-abstract-full').style.display = 'inline'; document.getElementById('2407.01621v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.01621v1-abstract-full" style="display: none;"> Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much attention but remain extremely challenging. To address this challenge, we propose a framework named Interventional Dynamical Causality (IntDC) for such non-intervention systems, along with its computational criterion, Interventional Embedding Entropy (IEE), to quantify causality. The IEE criterion theoretically and numerically enables the deciphering of IntDC solely from observational (non-interventional) time-series data, without requiring any knowledge of dynamical models or real interventions in the considered system. Demonstrations of performance showed the accuracy and robustness of IEE on benchmark simulated systems as well as real-world systems, including the neural connectomes of C. elegans, COVID-19 transmission networks in Japan, and regulatory networks surrounding key circadian genes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.01621v1-abstract-full').style.display = 'none'; document.getElementById('2407.01621v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.17128">arXiv:2404.17128</a> <span> [<a href="https://arxiv.org/pdf/2404.17128">pdf</a>, <a href="https://arxiv.org/format/2404.17128">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Network Structure Governs Drosophila Brain Functionality </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Zhang%2C+X">Xiaoyu Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Yang%2C+P">Pengcheng Yang</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+J">Jiawei Feng</a>, <a href="/search/q-bio?searchtype=author&query=Luo%2C+Q">Qiang Luo</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+X">Xin Lu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.17128v4-abstract-short" style="display: inline;"> How intelligence emerges from living beings has been a fundamental question in neuroscience. However, it remains largely unanswered due to the complex neuronal dynamics and intricate connections between neurons in real neural systems. To address this challenge, we leveraged the largest available adult Drosophila connectome data set, and constructed a comprehensive computational framework based on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.17128v4-abstract-full').style.display = 'inline'; document.getElementById('2404.17128v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.17128v4-abstract-full" style="display: none;"> How intelligence emerges from living beings has been a fundamental question in neuroscience. However, it remains largely unanswered due to the complex neuronal dynamics and intricate connections between neurons in real neural systems. To address this challenge, we leveraged the largest available adult Drosophila connectome data set, and constructed a comprehensive computational framework based on simplified neuronal activation mechanisms to simulate the observed activation behavior within the connectome. The results revealed that even with rudimentary neuronal activation mechanisms, models grounded in real neural network structures can generate activation patterns strikingly similar to those observed in the actual brain. A significant discovery was the consistency of activation patterns across various neuronal dynamic models. This consistency, achieved with the same network structure, underscores the pivotal role of network topology in neural information processing. These results challenge the prevailing view that solely relies on neuron count or complex individual neuron dynamics. Further analysis demonstrated a near-complete separation of the visual and olfactory systems at the network level. Moreover, we found that the network distance, rather than spatial distance, is the primary determinant of activation patterns. Additionally, our experiments revealed that a reconnect rate of at least 0.1% was sufficient to disrupt the previously observed activation patterns. We also observed synergistic effects between the brain hemispheres: Even with unilateral input stimuli, visual-related neurons in both hemispheres were activated, highlighting the importance of interhemispheric communication. These findings emphasize the crucial role of network structure in neural activation and offer novel insights into the fundamental principles governing brain functionality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.17128v4-abstract-full').style.display = 'none'; document.getElementById('2404.17128v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.00962">arXiv:2404.00962</a> <span> [<a href="https://arxiv.org/pdf/2404.00962">pdf</a>, <a href="https://arxiv.org/format/2404.00962">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Diffusion-Driven Domain Adaptation for Generating 3D Molecules </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Hong%2C+H">Haokai Hong</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wanyu Lin</a>, <a href="/search/q-bio?searchtype=author&query=Tan%2C+K+C">Kay Chen Tan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.00962v1-abstract-short" style="display: inline;"> Can we train a molecule generator that can generate 3D molecules from a new domain, circumventing the need to collect data? This problem can be cast as the problem of domain adaptive molecule generation. This work presents a novel and principled diffusion-based approach, called GADM, that allows shifting a generative model to desired new domains without the need to collect even a single molecule.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00962v1-abstract-full').style.display = 'inline'; document.getElementById('2404.00962v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.00962v1-abstract-full" style="display: none;"> Can we train a molecule generator that can generate 3D molecules from a new domain, circumventing the need to collect data? This problem can be cast as the problem of domain adaptive molecule generation. This work presents a novel and principled diffusion-based approach, called GADM, that allows shifting a generative model to desired new domains without the need to collect even a single molecule. As the domain shift is typically caused by the structure variations of molecules, e.g., scaffold variations, we leverage a designated equivariant masked autoencoder (MAE) along with various masking strategies to capture the structural-grained representations of the in-domain varieties. In particular, with an asymmetric encoder-decoder module, the MAE can generalize to unseen structure variations from the target domains. These structure variations are encoded with an equivariant encoder and treated as domain supervisors to control denoising. We show that, with these encoded structural-grained domain supervisors, GADM can generate effective molecules within the desired new domains. We conduct extensive experiments across various domain adaptation tasks over benchmarking datasets. We show that our approach can improve up to 65.6% in terms of success rate defined based on molecular validity, uniqueness, and novelty compared to alternative baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00962v1-abstract-full').style.display = 'none'; document.getElementById('2404.00962v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 3 figures, and 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.06959">arXiv:2401.06959</a> <span> [<a href="https://arxiv.org/pdf/2401.06959">pdf</a>, <a href="https://arxiv.org/format/2401.06959">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.xcrp.2025.102405">10.1016/j.xcrp.2025.102405 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Quantifying energy landscape of high-dimensional oscillatory systems by diffusion decomposition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Bian%2C+S">Shirui Bian</a>, <a href="/search/q-bio?searchtype=author&query=Zhou%2C+R">Ruisong Zhou</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+C">Chunhe Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.06959v2-abstract-short" style="display: inline;"> High-dimensional networks producing oscillatory dynamics are ubiquitous in biological systems. Unravelling the mechanism of oscillatory dynamics in biological networks with stochastic perturbations becomes paramountly significant. Although the classical energy landscape theory provides a tool to study this problem in multistable systems and explain cellular functions, it remains challenging to qua… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06959v2-abstract-full').style.display = 'inline'; document.getElementById('2401.06959v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.06959v2-abstract-full" style="display: none;"> High-dimensional networks producing oscillatory dynamics are ubiquitous in biological systems. Unravelling the mechanism of oscillatory dynamics in biological networks with stochastic perturbations becomes paramountly significant. Although the classical energy landscape theory provides a tool to study this problem in multistable systems and explain cellular functions, it remains challenging to quantify the landscape for high-dimensional oscillatory systems accurately. Here we propose an approach called the diffusion decomposition of Gaussian approximation (DDGA). We demonstrate the efficacy of the DDGA in quantifying the energy landscape of oscillatory systems and corresponding stochastic dynamics, in comparison with existing approaches. By further applying the DDGA to high-dimensional biological networks, we are able to uncover more intricate biological mechanisms efficiently, which deepens our understanding of cellular functions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06959v2-abstract-full').style.display = 'none'; document.getElementById('2401.06959v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Cell Reports Physical Science, 6, 102405, (2025) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.01383">arXiv:2401.01383</a> <span> [<a href="https://arxiv.org/pdf/2401.01383">pdf</a>, <a href="https://arxiv.org/format/2401.01383">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Pistos%2C+M">Michalis Pistos</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+G">Gang Li</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Weili Lin</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+D">Dinggang Shen</a>, <a href="/search/q-bio?searchtype=author&query=Rekik%2C+I">Islem Rekik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.01383v2-abstract-short" style="display: inline;"> The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Existing deep learning solutions suffer from three major limitations. First, they cannot generalize to multi-trajectory prediction tasks, where each graph trajectory corresponds to a particular imaging modality or co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.01383v2-abstract-full').style.display = 'inline'; document.getElementById('2401.01383v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.01383v2-abstract-full" style="display: none;"> The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Existing deep learning solutions suffer from three major limitations. First, they cannot generalize to multi-trajectory prediction tasks, where each graph trajectory corresponds to a particular imaging modality or connectivity type (e.g., T1-w MRI). Second, existing models require extensive training datasets to achieve satisfactory performance which are often challenging to obtain. Third, they do not efficiently utilize incomplete time series data. To address these limitations, we introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets. As a result, we enhance the performance of each hospital's local generative model, while preserving data privacy. The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations. Our comprehensive experimental results showed the outperformance of FedGmTE-Net++ in brain multi-trajectory prediction from a single baseline graph in comparison with benchmark methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.01383v2-abstract-full').style.display = 'none'; document.getElementById('2401.01383v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.16713">arXiv:2308.16713</a> <span> [<a href="https://arxiv.org/pdf/2308.16713">pdf</a>, <a href="https://arxiv.org/format/2308.16713">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Jing%2C+H">Hongtai Jing</a>, <a href="/search/q-bio?searchtype=author&query=Gao%2C+Z">Zhengtao Gao</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+S">Sheng Xu</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+T">Tao Shen</a>, <a href="/search/q-bio?searchtype=author&query=Peng%2C+Z">Zhangzhi Peng</a>, <a href="/search/q-bio?searchtype=author&query=He%2C+S">Shwai He</a>, <a href="/search/q-bio?searchtype=author&query=You%2C+T">Tao You</a>, <a href="/search/q-bio?searchtype=author&query=Ye%2C+S">Shuang Ye</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a>, <a href="/search/q-bio?searchtype=author&query=Sun%2C+S">Siqi Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.16713v1-abstract-short" style="display: inline;"> In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.16713v1-abstract-full').style.display = 'inline'; document.getElementById('2308.16713v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.16713v1-abstract-full" style="display: none;"> In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% non-redundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold, and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.16713v1-abstract-full').style.display = 'none'; document.getElementById('2308.16713v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.01495">arXiv:2212.01495</a> <span> [<a href="https://arxiv.org/pdf/2212.01495">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/bioadv/vbad043">10.1093/bioadv/vbad043 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+J">Jiahao Li</a>, <a href="/search/q-bio?searchtype=author&query=Wu%2C+Z">Zhourun Wu</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wenhao Lin</a>, <a href="/search/q-bio?searchtype=author&query=Luo%2C+J">Jiawei Luo</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+Q">Qingcai Chen</a>, <a href="/search/q-bio?searchtype=author&query=Chen%2C+J">Junjie Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.01495v2-abstract-short" style="display: inline;"> Motivation: Enhancers are important cis-regulatory elements that regulate a wide range of biological functions and enhance the transcription of target genes. Although many feature extraction methods have been proposed to improve the performance of enhancer identification, they cannot learn position-related multiscale contextual information from raw DNA sequences. Results: In this article, we pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.01495v2-abstract-full').style.display = 'inline'; document.getElementById('2212.01495v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.01495v2-abstract-full" style="display: none;"> Motivation: Enhancers are important cis-regulatory elements that regulate a wide range of biological functions and enhance the transcription of target genes. Although many feature extraction methods have been proposed to improve the performance of enhancer identification, they cannot learn position-related multiscale contextual information from raw DNA sequences. Results: In this article, we propose a novel enhancer identification method (iEnhancer-ELM) based on BERT-like enhancer language models. iEnhancer-ELM tokenizes DNA sequences with multi-scale k-mers and extracts contextual information of different scale k-mers related with their positions via an multi-head attention mechanism. We first evaluate the performance of different scale k-mers, then ensemble them to improve the performance of enhancer identification. The experimental results on two popular benchmark datasets show that our model outperforms stateof-the-art methods. We further illustrate the interpretability of iEnhancer-ELM. For a case study, we discover 30 enhancer motifs via a 3-mer-based model, where 12 of motifs are verified by STREME and JASPAR, demonstrating our model has a potential ability to unveil the biological mechanism of enhancer. Availability and implementation: The models and associated code are available at https://github.com/chen-bioinfo/iEnhancer-ELM Contact: junjiechen@hit.edu.cn Supplementary information: Supplementary data are available at Bioinformatics Advances online. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.01495v2-abstract-full').style.display = 'none'; document.getElementById('2212.01495v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 5 figures. It is a new accepted version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.09470">arXiv:2210.09470</a> <span>  </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> </div> </div> <p class="title is-5 mathjax"> Biomass transfer on autocatalytic reaction network: a delay differential equation formulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei-Hsiang Lin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.09470v2-abstract-short" style="display: inline;"> For a biological system to grow, the biomass must be incorporated, transferred, and accumulated into the underlying reaction network. There are two perspectives for studying growth dynamics of reaction networks: one way is to focus on each node in the networks and study its associated influxes and effluxes. The other way is to focus on a fraction of biomass and study its trajectory along the react… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.09470v2-abstract-full').style.display = 'inline'; document.getElementById('2210.09470v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.09470v2-abstract-full" style="display: none;"> For a biological system to grow, the biomass must be incorporated, transferred, and accumulated into the underlying reaction network. There are two perspectives for studying growth dynamics of reaction networks: one way is to focus on each node in the networks and study its associated influxes and effluxes. The other way is to focus on a fraction of biomass and study its trajectory along the reaction pathways. The former perspective (analogous to the "Eulerian representation" in fluid mechanics) has been studied extensively, while the latter perspective (analogous to the "Lagrangian representation" in fluid mechanics) has not been systematically explored. In this work, I characterized the biomass transfer process for autocatalytic, growing systems with scalable reaction fluxes. Under balanced growth, the long-term growth dynamics of the systems are described by delay differential equations (DDEs). The kernel function of the DDE serves as a unique pattern for the catalytic delay for a reaction network, and in frequency domain the delay spectrum provides a geometric interpretation for long-term growth rate. The DDE formulation provides a clear intuition on how autocatalytic reaction pathways lead to system growth, it also enables us to classify and compare reaction networks with different network structures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.09470v2-abstract-full').style.display = 'none'; document.getElementById('2210.09470v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Error in the text</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.14006">arXiv:2203.14006</a> <span> [<a href="https://arxiv.org/pdf/2203.14006">pdf</a>, <a href="https://arxiv.org/format/2203.14006">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Continuity scaling: A rigorous framework for detecting and quantifying causality accurately </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Ying%2C+X">Xiong Ying</a>, <a href="/search/q-bio?searchtype=author&query=Leng%2C+S">Si-Yang Leng</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+H">Huan-Fei Ma</a>, <a href="/search/q-bio?searchtype=author&query=Nie%2C+Q">Qing Nie</a>, <a href="/search/q-bio?searchtype=author&query=Lai%2C+Y">Ying-Cheng Lai</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.14006v1-abstract-short" style="display: inline;"> Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.14006v1-abstract-full').style.display = 'inline'; document.getElementById('2203.14006v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.14006v1-abstract-full" style="display: none;"> Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross map as conventionally implemented, we define causation through measuring the {\it scaling law} for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.14006v1-abstract-full').style.display = 'none'; document.getElementById('2203.14006v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 figures; The article has been peer reviewed and accepted by RESEARCH</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.03535">arXiv:2110.03535</a> <span> [<a href="https://arxiv.org/pdf/2110.03535">pdf</a>, <a href="https://arxiv.org/format/2110.03535">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Bessadok%2C+A">Alaa Bessadok</a>, <a href="/search/q-bio?searchtype=author&query=Nebli%2C+A">Ahmed Nebli</a>, <a href="/search/q-bio?searchtype=author&query=Mahjoub%2C+M+A">Mohamed Ali Mahjoub</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+G">Gang Li</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Weili Lin</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+D">Dinggang Shen</a>, <a href="/search/q-bio?searchtype=author&query=Rekik%2C+I">Islem Rekik</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.03535v1-abstract-short" style="display: inline;"> Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of works has focused on predicting baby brain evoluti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.03535v1-abstract-full').style.display = 'inline'; document.getElementById('2110.03535v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.03535v1-abstract-full" style="display: none;"> Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of works has focused on predicting baby brain evolution trajectory from a neonatal brain connectome derived from a single modality. Although promising, large training datasets are essential to boost model learning and to generalize to a multi-trajectory prediction from different modalities (i.e., functional and morphological connectomes). Here, we unprecedentedly explore the question: Can we design a few-shot learning-based framework for predicting brain graph trajectories across different modalities? To this aim, we propose a Graph Multi-Trajectory Evolution Network (GmTE-Net), which adopts a teacher-student paradigm where the teacher network learns on pure neonatal brain graphs and the student network learns on simulated brain graphs given a set of different timepoints. To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs). To boost the performance of the student network, we introduce a local topology-aware distillation loss that forces the predicted graph topology of the student network to be consistent with the teacher network. Experimental results demonstrate substantial performance gains over benchmark methods. Hence, our GmTE-Net can be leveraged to predict atypical brain connectivity trajectory evolution across various modalities. Our code is available at https: //github.com/basiralab/GmTE-Net. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.03535v1-abstract-full').style.display = 'none'; document.getElementById('2110.03535v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.06899">arXiv:2009.06899</a> <span> [<a href="https://arxiv.org/pdf/2009.06899">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Co-evolution of Functional Brain Network at Multiple Scales during Early Infancy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wen%2C+X">Xuyun Wen</a>, <a href="/search/q-bio?searchtype=author&query=Hsu%2C+L">Liming Hsu</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Weili Lin</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+H">Han Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Shen%2C+D">Dinggang Shen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.06899v1-abstract-short" style="display: inline;"> The human brains are organized into hierarchically modular networks facilitating efficient and stable information processing and supporting diverse cognitive processes during the course of development. While the remarkable reconfiguration of functional brain network has been firmly established in early life, all these studies investigated the network development from a "single-scale" perspective,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.06899v1-abstract-full').style.display = 'inline'; document.getElementById('2009.06899v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.06899v1-abstract-full" style="display: none;"> The human brains are organized into hierarchically modular networks facilitating efficient and stable information processing and supporting diverse cognitive processes during the course of development. While the remarkable reconfiguration of functional brain network has been firmly established in early life, all these studies investigated the network development from a "single-scale" perspective, which ignore the richness engendered by its hierarchical nature. To fill this gap, this paper leveraged a longitudinal infant resting-state functional magnetic resonance imaging dataset from birth to 2 years of age, and proposed an advanced methodological framework to delineate the multi-scale reconfiguration of functional brain network during early development. Our proposed framework is consist of two parts. The first part developed a novel two-step multi-scale module detection method that could uncover efficient and consistent modular structure for longitudinal dataset from multiple scales in a completely data-driven manner. The second part designed a systematic approach that employed the linear mixed-effect model to four global and nodal module-related metrics to delineate scale-specific age-related changes of network organization. By applying our proposed methodological framework on the collected longitudinal infant dataset, we provided the first evidence that, in the first 2 years of life, the brain functional network is co-evolved at different scales, where each scale displays the unique reconfiguration pattern in terms of modular organization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.06899v1-abstract-full').style.display = 'none'; document.getElementById('2009.06899v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.03000">arXiv:1711.03000</a> <span> [<a href="https://arxiv.org/pdf/1711.03000">pdf</a>, <a href="https://arxiv.org/format/1711.03000">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1101/2020.06.16.155630">10.1101/2020.06.16.155630 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Differential covariance: A new method to estimate functional connectivity in fMRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lin%2C+T+w">Tiger w. Lin</a>, <a href="/search/q-bio?searchtype=author&query=Krishnan%2C+G+P">Giri P. Krishnan</a>, <a href="/search/q-bio?searchtype=author&query=Bazhenov%2C+M">Maxim Bazhenov</a>, <a href="/search/q-bio?searchtype=author&query=Sejnowski%2C+T+J">Terrence J. Sejnowski</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.03000v1-abstract-short" style="display: inline;"> Measuring functional connectivity from fMRI is important in understanding processing in cortical networks. However, because brain's connection pattern is complex, currently used methods are prone to produce false connections. We introduce here a new method that uses derivative for estimating functional connectivity. Using simulations, we benchmarked our method with other commonly used methods. Our… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.03000v1-abstract-full').style.display = 'inline'; document.getElementById('1711.03000v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.03000v1-abstract-full" style="display: none;"> Measuring functional connectivity from fMRI is important in understanding processing in cortical networks. However, because brain's connection pattern is complex, currently used methods are prone to produce false connections. We introduce here a new method that uses derivative for estimating functional connectivity. Using simulations, we benchmarked our method with other commonly used methods. Our method achieves better results in complex network simulations. This new method provides an alternative way to estimate functional connectivity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.03000v1-abstract-full').style.display = 'none'; document.getElementById('1711.03000v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:1706.02451</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1706.02451">arXiv:1706.02451</a> <span> [<a href="https://arxiv.org/pdf/1706.02451">pdf</a>, <a href="https://arxiv.org/format/1706.02451">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lin%2C+T+W">Tiger W. Lin</a>, <a href="/search/q-bio?searchtype=author&query=Das%2C+A">Anup Das</a>, <a href="/search/q-bio?searchtype=author&query=Krishnan%2C+G+P">Giri P. Krishnan</a>, <a href="/search/q-bio?searchtype=author&query=Bazhenov%2C+M">Maxim Bazhenov</a>, <a href="/search/q-bio?searchtype=author&query=Sejnowski%2C+T+J">Terrence J. Sejnowski</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1706.02451v1-abstract-short" style="display: inline;"> With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship between multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson et al., 2008), th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.02451v1-abstract-full').style.display = 'inline'; document.getElementById('1706.02451v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1706.02451v1-abstract-full" style="display: none;"> With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship between multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson et al., 2008), they produce spurious connections. The general linear model (GLM), which models spikes trains as Poisson processes (Okatan et al., 2005; Truccolo et al., 2005; Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential (LFP) recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved better or similar performance to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.02451v1-abstract-full').style.display = 'none'; document.getElementById('1706.02451v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1704.01369">arXiv:1704.01369</a> <span> [<a href="https://arxiv.org/pdf/1704.01369">pdf</a>, <a href="https://arxiv.org/ps/1704.01369">ps</a>, <a href="https://arxiv.org/format/1704.01369">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1103/PhysRevLett.119.138102">10.1103/PhysRevLett.119.138102 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Angstrom-resolution single-molecule fluorescence resonance energy transfer reveals mechanisms of DNA helicases </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wenxia Lin</a>, <a href="/search/q-bio?searchtype=author&query=Ma%2C+J">Jianbing Ma</a>, <a href="/search/q-bio?searchtype=author&query=Nong%2C+D">Daguan Nong</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+C">Chunhua Xu</a>, <a href="/search/q-bio?searchtype=author&query=Zhang%2C+B">Bo Zhang</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+J">Jinghua Li</a>, <a href="/search/q-bio?searchtype=author&query=Jia%2C+Q">Qi Jia</a>, <a href="/search/q-bio?searchtype=author&query=Dou%2C+S">Shuoxing Dou</a>, <a href="/search/q-bio?searchtype=author&query=Xi%2C+X">Xuguang Xi</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+Y">Ying Lu</a>, <a href="/search/q-bio?searchtype=author&query=Li%2C+M">Ming Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1704.01369v1-abstract-short" style="display: inline;"> Single-molecule FRET is widely used to study helicases by detecting distance changes between a fluorescent donor and an acceptor anchored to overhangs of a forked DNA duplex. However, it has lacked single-base pair (1-bp) resolution required for revealing stepping dynamics in unwinding because FRET signals are usually blurred by thermal fluctuations of the overhangs. We designed a nanotensioner in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.01369v1-abstract-full').style.display = 'inline'; document.getElementById('1704.01369v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1704.01369v1-abstract-full" style="display: none;"> Single-molecule FRET is widely used to study helicases by detecting distance changes between a fluorescent donor and an acceptor anchored to overhangs of a forked DNA duplex. However, it has lacked single-base pair (1-bp) resolution required for revealing stepping dynamics in unwinding because FRET signals are usually blurred by thermal fluctuations of the overhangs. We designed a nanotensioner in which a short DNA is bent to exert a force on the overhangs, just as in optical/magnetic tweezers. The strategy improved the resolution of FRET to 0.5 bp, high enough to uncover the differences in DNA unwinding by yeast Pif1 and E. coli RecQ whose unwinding behaviors cannot be differentiated by currently practiced methods. We found that Pif1 exhibits 1-bp-stepping kinetics, while RecQ breaks 1 bp at a time but questers the nascent nucleotides and releases them randomly. The high-resolution data allowed us to propose a three-parameter model to quantitatively interpret the apparently different unwinding behaviors of the two helicases which belong to two superfamilies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.01369v1-abstract-full').style.display = 'none'; document.getElementById('1704.01369v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Lett. 119, 138102 (2017) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1604.03187">arXiv:1604.03187</a> <span> [<a href="https://arxiv.org/pdf/1604.03187">pdf</a>, <a href="https://arxiv.org/ps/1604.03187">ps</a>, <a href="https://arxiv.org/format/1604.03187">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chaotic Dynamics">nlin.CD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Evoking complex neuronal networks by stimulating a single neuron </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+M">Mengjiao Chen</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Weijie Lin</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+H">Hengtong Wang</a>, <a href="/search/q-bio?searchtype=author&query=Ren%2C+W">Wei Ren</a>, <a href="/search/q-bio?searchtype=author&query=Wang%2C+X">Xingang Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1604.03187v1-abstract-short" style="display: inline;"> The dynamical responses of complex neuronal networks to external stimulus injected on a \emph{single} neuron are investigated. Stimulating the largest-degree neuron in the network, it is found that as the intensity of the stimulus increases, the network will be transiting from the resting to firing states and then restoring to the resting state, showing a bounded firing region in the parameter spa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1604.03187v1-abstract-full').style.display = 'inline'; document.getElementById('1604.03187v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1604.03187v1-abstract-full" style="display: none;"> The dynamical responses of complex neuronal networks to external stimulus injected on a \emph{single} neuron are investigated. Stimulating the largest-degree neuron in the network, it is found that as the intensity of the stimulus increases, the network will be transiting from the resting to firing states and then restoring to the resting state, showing a bounded firing region in the parameter space. Furthermore, it is found that as the coupling strength decreases, the firing region is gradually expanded and, at the weak couplings, separated into disconnected subregions. By a simplified network model, we conduct a detail analysis on the bifurcation diagram of the network dynamics in the two-dimensional parameter space spanned by stimulating intensity and coupling strength, and, by introducing a new coefficient named effective stimulus, explore the mechanisms of the modified firing region. It is revealed that the coupling strength and stimulating intensity are equally important in evoking the network, but with different mechanisms. Specifically, the effective stimuli are \emph{shifted up} globally with the increase of the stimulating intensity, while are \emph{drawn closer} with the increase of the coupling strength. The dynamical responses of small-world and random complex networks to external stimulus injected on the largest-degree neuron are also investigated, which confirm the generality of the observed phenomena. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1604.03187v1-abstract-full').style.display = 'none'; document.getElementById('1604.03187v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 April, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 figures, 8 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1203.0875">arXiv:1203.0875</a> <span> [<a href="https://arxiv.org/pdf/1203.0875">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Molecular Networks">q-bio.MN</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1371/journal.pcbi.1002094">10.1371/journal.pcbi.1002094 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Dynamical Model Reveals Gene Co-Localizations in Nucleus </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kang%2C+J">Jing Kang</a>, <a href="/search/q-bio?searchtype=author&query=Xu%2C+B">Bing Xu</a>, <a href="/search/q-bio?searchtype=author&query=Yao%2C+Y">Ye Yao</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a>, <a href="/search/q-bio?searchtype=author&query=Hennessy%2C+C">Conor Hennessy</a>, <a href="/search/q-bio?searchtype=author&query=Fraser%2C+P">Peter Fraser</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+J">Jianfeng Feng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1203.0875v1-abstract-short" style="display: inline;"> Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferentia… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1203.0875v1-abstract-full').style.display = 'inline'; document.getElementById('1203.0875v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1203.0875v1-abstract-full" style="display: none;"> Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferential co-localization is possible for co-regulated genes without any direct interaction, and suggests the occurrence may be due to a limitation in the number of available transcription factors. Experimental data of chromatin movements demonstrates that fractional rather than standard Brownian motion is more appropriate to model gene mobilizations, and we tested our dynamical model against recent static experimental data, using a sub-diffusion process by which the genes tend to colocalize more easily. Moreover, in order to compare our model with recently obtained experimental data, we studied the association level between genes and factors, and presented data supporting the validation of this dynamic model. As further applications of our model, we applied it to test against more biological observations. We found that increasing transcription factor number, rather than factory number and nucleus size, might be the reason for decreasing gene co-localization. In the scenario of frequency- or amplitude-modulation of transcription factors, our model predicted that frequency-modulation may increase the co-localization between its targeted genes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1203.0875v1-abstract-full').style.display = 'none'; document.getElementById('1203.0875v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 March, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 7 figures; PloS Computational Biology 2011</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1201.0246">arXiv:1201.0246</a> <span> [<a href="https://arxiv.org/pdf/1201.0246">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1371/journal.pone.0038402">10.1371/journal.pone.0038402 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Bifurcations of Emergent Bursting in a Neuronal Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Wu%2C+Y">Yu Wu</a>, <a href="/search/q-bio?searchtype=author&query=Lu%2C+W">Wenlian Lu</a>, <a href="/search/q-bio?searchtype=author&query=Lin%2C+W">Wei Lin</a>, <a href="/search/q-bio?searchtype=author&query=Leng%2C+G">Gareth Leng</a>, <a href="/search/q-bio?searchtype=author&query=Feng%2C+J">Jianfeng Feng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1201.0246v1-abstract-short" style="display: inline;"> Currently we routinely develop a complex neuronal network to explain observed but often paradoxical phenomena based upon biological recordings. Here we present a general approach to demonstrate how to mathematically tackle such a complex neuronal network so that we can fully understand the underlying mechanism. Using an oxytocin network developed earlier as an example, we show how we can reduce a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1201.0246v1-abstract-full').style.display = 'inline'; document.getElementById('1201.0246v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1201.0246v1-abstract-full" style="display: none;"> Currently we routinely develop a complex neuronal network to explain observed but often paradoxical phenomena based upon biological recordings. Here we present a general approach to demonstrate how to mathematically tackle such a complex neuronal network so that we can fully understand the underlying mechanism. Using an oxytocin network developed earlier as an example, we show how we can reduce a complex model with many variables to a tractable model with two variables, while retaining all key qualitative features of the model. The approach enables us to uncover how emergent synchronous bursting could arise from a neuronal network which embodies all known biological features. Surprisingly, the discovered mechanisms for bursting are similar to those found in other systems reported in the literature, and illustrate a generic way to exhibit emergent and multi-time scale spikes: at the membrane potential level and the firing rate level. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1201.0246v1-abstract-full').style.display = 'none'; document.getElementById('1201.0246v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 December, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 figures, 1 table</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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