{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:53:15Z","timestamp":1770817995391,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: The phenotypes of knockout mice provide crucial information for understanding the biological functions of mammalian genes. Among various knockout phenotypes, lethality is of great interest because those involved genes play essential roles. With the availability of large-scale genomic data, we aimed to assess how well the integration of various genomic features can predict the lethal phenotype of single-gene knockout mice.<\/jats:p><jats:p>Results: We first assembled a comprehensive list of 491 candidate genomic features derived from diverse data sources. Using mouse genes with a known phenotype as the training set, we integrated the informative genomic features to predict the knockout lethality through three machine learning methods. Based on cross-validation, our models could achieve a good performance (accuracy = 73% and recall = 63%). Our results serve as a valuable practical resource in the mouse genetics research community, and also accelerate the translation of the knowledge of mouse genes into better strategies for studying human disease.<\/jats:p><jats:p>Contact: \u00a0hliang1@mdanderson.org<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts120","type":"journal-article","created":{"date-parts":[[2012,3,15]],"date-time":"2012-03-15T05:33:56Z","timestamp":1331789636000},"page":"1246-1252","source":"Crossref","is-referenced-by-count":35,"title":["Predicting the lethal phenotype of the knockout mouse by integrating comprehensive genomic data"],"prefix":"10.1093","volume":"28","author":[{"given":"Yuan","family":"Yuan","sequence":"first","affiliation":[{"name":"1 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, 2Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, 3Department of Statistics, Rice University, Houston, TX 77005, 4Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61801 And 5Department of Statistics, Texas A&M University, College Station, TX 77843, USA"},{"name":"1 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, 2Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, 3Department of Statistics, Rice University, Houston, TX 77005, 4Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61801 And 5Department of Statistics, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Yanxun","family":"Xu","sequence":"additional","affiliation":[{"name":"1 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, 2Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, 3Department of Statistics, Rice University, Houston, TX 77005, 4Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61801 And 5Department of Statistics, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Jianfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"1 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, 2Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, 3Department of Statistics, Rice University, Houston, TX 77005, 4Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61801 And 5Department of Statistics, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Robyn L.","family":"Ball","sequence":"additional","affiliation":[{"name":"1 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, 2Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, 3Department of Statistics, Rice University, Houston, TX 77005, 4Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61801 And 5Department of Statistics, Texas A&M University, College Station, TX 77843, USA"}]},{"given":"Han","family":"Liang","sequence":"additional","affiliation":[{"name":"1 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, 2Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, 3Department of Statistics, Rice University, Houston, TX 77005, 4Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61801 And 5Department of Statistics, Texas A&M University, College Station, TX 77843, USA"}]}],"member":"286","published-online":{"date-parts":[[2012,3,13]]},"reference":[{"key":"2023012512231391200_B1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1038\/nrg1272","article-title":"Network biology: understanding the cell's functional organization","volume":"5","author":"Barabasi","year":"2004","journal-title":"Nat. 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