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is-small is-grey tooltip is-tooltip-top" data-tooltip="Category Theory">math.CT</span> </div> </div> <p class="title is-5 mathjax"> Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Warrell%2C+J">Jonathan Warrell</a>, <a href="/search/q-bio?searchtype=author&query=Alesiani%2C+F">Francesco Alesiani</a>, <a href="/search/q-bio?searchtype=author&query=Smith%2C+C">Cameron Smith</a>, <a href="/search/q-bio?searchtype=author&query=M%C3%B6sch%2C+A">Anja M枚sch</a>, <a href="/search/q-bio?searchtype=author&query=Min%2C+M+R">Martin Renqiang Min</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="2411.09779v1-abstract-short" style="display: inline;"> Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms over arbitrarily many levels based on concepts from category theory and populatio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09779v1-abstract-full').style.display = 'inline'; document.getElementById('2411.09779v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09779v1-abstract-full" style="display: none;"> Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms over arbitrarily many levels based on concepts from category theory and population genetics. We formulate a multilevel version of the Wright-Fisher process using this approach, and we show that this model can be analyzed to clarify key features of multilevel selection. Particularly, we derive an extended multilevel probabilistic version of Price's Equation via the Kantorovich Monad, and we use this to characterize regimes of parameter space within which selection acts antagonistically or cooperatively across levels. Finally, we show how our framework can provide a unified setting for learning genetic algorithms (GAs), and we show how we can use a Variational Optimization and a multi-level analogue of coalescent analysis to fit multilevel GAs to simulated data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09779v1-abstract-full').style.display = 'none'; document.getElementById('2411.09779v1-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> 14 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">18 pages, 3 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/2303.02162">arXiv:2303.02162</a> <span> [<a href="https://arxiv.org/pdf/2303.02162">pdf</a>, <a href="https://arxiv.org/format/2303.02162">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+Z">Ziqi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Min%2C+M+R">Martin Renqiang Min</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+H">Hongyu Guo</a>, <a href="/search/q-bio?searchtype=author&query=Cheng%2C+C">Chao Cheng</a>, <a href="/search/q-bio?searchtype=author&query=Clancy%2C+T">Trevor Clancy</a>, <a href="/search/q-bio?searchtype=author&query=Ning%2C+X">Xia Ning</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="2303.02162v1-abstract-short" style="display: inline;"> T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.02162v1-abstract-full').style.display = 'inline'; document.getElementById('2303.02162v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.02162v1-abstract-full" style="display: none;"> T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (RL) problem, and presented a framework TCRPPO with a mutation policy using proximal policy optimization. TCRPPO mutates TCRs into effective ones that can recognize given peptides. TCRPPO leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared TCRPPO with multiple baseline methods and demonstrated that TCRPPO significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of TCRPPO for both precision immunotherapy and peptide-recognizing TCR motif discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.02162v1-abstract-full').style.display = 'none'; document.getElementById('2303.02162v1-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> 2 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.08171">arXiv:2210.08171</a> <span> [<a href="https://arxiv.org/pdf/2210.08171">pdf</a>, <a href="https://arxiv.org/format/2210.08171">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"> Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Li%2C+T">Tianxiao Li</a>, <a href="/search/q-bio?searchtype=author&query=Guo%2C+H">Hongyu Guo</a>, <a href="/search/q-bio?searchtype=author&query=Grazioli%2C+F">Filippo Grazioli</a>, <a href="/search/q-bio?searchtype=author&query=Gerstein%2C+M">Mark Gerstein</a>, <a href="/search/q-bio?searchtype=author&query=Min%2C+M+R">Martin Renqiang Min</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="2210.08171v2-abstract-short" style="display: inline;"> In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally non-trivial, and requires significant domain knowledge. To automat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08171v2-abstract-full').style.display = 'inline'; document.getElementById('2210.08171v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.08171v2-abstract-full" style="display: none;"> In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally non-trivial, and requires significant domain knowledge. To automate this process from a data-driven perspective, we propose a disentangled Wasserstein autoencoder with an auxiliary classifier, which isolates the function-related patterns from the rest with theoretical guarantees. This enables one-pass protein sequence editing and improves the understanding of the resulting sequences and editing actions involved. To demonstrate its effectiveness, we apply it to T-cell receptors (TCRs), a well-studied structure-function case. We show that our method can be used to alter the function of TCRs without changing the structural backbone, outperforming several competing methods in generation quality and efficiency, and requiring only 10% of the running time needed by baseline models. To our knowledge, this is the first approach that utilizes disentangled representations for TCR engineering. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08171v2-abstract-full').style.display = 'none'; document.getElementById('2210.08171v2-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.02840">arXiv:2012.02840</a> <span> [<a href="https://arxiv.org/pdf/2012.02840">pdf</a>, <a href="https://arxiv.org/format/2012.02840">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="Artificial Intelligence">cs.AI</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"> Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Chen%2C+Z">Ziqi Chen</a>, <a href="/search/q-bio?searchtype=author&query=Min%2C+M+R">Martin Renqiang Min</a>, <a href="/search/q-bio?searchtype=author&query=Ning%2C+X">Xia Ning</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="2012.02840v2-abstract-short" style="display: inline;"> T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been develope… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.02840v2-abstract-full').style.display = 'inline'; document.getElementById('2012.02840v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.02840v2-abstract-full" style="display: none;"> T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network (CNN)-based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.02840v2-abstract-full').style.display = 'none'; document.getElementById('2012.02840v2-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 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">17 pages, 5 figures</span> </p> </li> </ol> <div 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