Document Zbl 1304.62132

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Appl. Stat.</a> <a href="/?q=in%3A339174" title="Articles in this Issue">8, No. 3, 1750-1781 (2014)</a>. </div> <div class="abstract">Summary: We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [<span class="zbmathjax-textit">H. A. Chipman</span>, Ann. Appl. Stat. 4, No. 1, 266–298 (2010; <a href="/1189.62066">Zbl 1189.62066</a>)] provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (<span class="zbmathjax-textit">Saccharomyces cerevisiae</span>). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods. The methods developed in this work are readily available in the R package bartMachine.</div> <div class="clear"></div> <br> <div class="citations"><div class="clear"><a href="/?q=rf%3A6381474">Cited in <strong>28</strong> Documents</a></div></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A62P10" title="MSC2020">62P10</a> </td> <td class="space"> Applications of statistics to biology and medical sciences; meta analysis </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A92D10" title="MSC2020">92D10</a> </td> <td class="space"> Genetics and epigenetics </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62F15" title="MSC2020">62F15</a> </td> <td class="space"> Bayesian inference </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62G08" title="MSC2020">62G08</a> </td> <td class="space"> Nonparametric regression and quantile regression </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3Avariable+selection">variable selection</a>; <a href="/?q=ut%3Anonparametric+regression">nonparametric regression</a>; <a href="/?q=ut%3ABayesian+learning">Bayesian learning</a>; <a href="/?q=ut%3Amachine+learning">machine learning</a>; <a href="/?q=ut%3Apermutation+testing">permutation testing</a>; <a href="/?q=ut%3Adecision+trees">decision trees</a>; <a href="/?q=ut%3Agene+regulatory+network">gene regulatory network</a></div><div class="keywords"> <h3>Citations:</h3><a href="/1189.62066">Zbl 1189.62066</a></div> <div class="software"> <h3>Software:</h3><a href="/software/31733">EMVS</a>; <a href="/software/8254">GeneSrF</a>; <a href="/software/7794">SSS</a>; <a href="/software/10962">bartMachine</a>; <a href="/software/7995">BayesTree</a>; <a href="/software/8159">glmnet</a>; <a href="/software/40584">BartPy</a>; <a href="/software/14503">spikeslab</a>; <a href="/software/771">R</a></div>  <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;">  </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function">  <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 1304.62132" data-ciurl="/ci/06381474" data-biburl="/bibtex/06381474.bib" data-amsurl="/amsrefs/06381474.bib" data-xmlurl="/xml/06381474.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/06381474.pdf" title="Zbl 1304.62132 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1214/14-AOAS755" aria-label="DOI for “Variable selection for BART: an application to gene regulation”" title="10.1214/14-AOAS755">DOI</a> <a class="btn btn-default btn-xs" type="button" href="https://arxiv.org/abs/1310.4887"title="Note: arXiv document may differ from published version">arXiv</a> <a class="btn btn-default btn-xs" type="button" href="https://projecteuclid.org/euclid.aoas/1414091233" title="Full Text at Project Euclid">Euclid</a> </div> <div class="sfx" style="float: right;"> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Bleich, J., Kapelner, A., George, E. and Jensen, S. 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