Yuanfang Guan1, Tingyang Li1, Hongjiu Zhang1, Fan Zhu2, Gilbert S Omenn1,3. 1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. 2. Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China. 3. Departments of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Abstract
Motivation: Finding driver genes that are responsible for the aberrant proliferation rate of cancer cells is informative for both cancer research and the development of targeted drugs. The established experimental and computational methods are labor-intensive. To make algorithms feasible in real clinical settings, methods that can predict driver genes using less experimental data are urgently needed. Results: We designed an effective feature selection method and used Support Vector Machines (SVM) to predict the essentiality of the potential driver genes in cancer cell lines with only 10 genes as features. The accuracy of our predictions was the highest in the Broad-DREAM Gene Essentiality Prediction Challenge. We also found a set of genes whose essentiality could be predicted much more accurately than others, which we called Accurately Predicted (AP) genes. Our method can serve as a new way of assessing the essentiality of genes in cancer cells. Availability and implementation: The raw data that support the findings of this study are available at Synapse. https://www.synapse.org/#! Synapse: syn2384331/wiki/62825. Source code is available at GitHub. https://github.com/GuanLab/DREAM-Gene-Essentiality-Challenge. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Finding driver genes that are responsible for the aberrant proliferation rate of cancer cells is informative for both cancer research and the development of targeted drugs. The established experimental and computational methods are labor-intensive. To make algorithms feasible in real clinical settings, methods that can predict driver genes using less experimental data are urgently needed. Results: We designed an effective feature selection method and used Support Vector Machines (SVM) to predict the essentiality of the potential driver genes in cancer cell lines with only 10 genes as features. The accuracy of our predictions was the highest in the Broad-DREAM Gene Essentiality Prediction Challenge. We also found a set of genes whose essentiality could be predicted much more accurately than others, which we called Accurately Predicted (AP) genes. Our method can serve as a new way of assessing the essentiality of genes in cancer cells. Availability and implementation: The raw data that support the findings of this study are available at Synapse. https://www.synapse.org/#! Synapse: syn2384331/wiki/62825. Source code is available at GitHub. https://github.com/GuanLab/DREAM-Gene-Essentiality-Challenge. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Denis Soulières; Fred R Hirsch; Frances A Shepherd; Walter Bordogna; Paul Delmar; David S Shames; Barbara Klughammer Journal: J Thorac Oncol Date: 2015-09 Impact factor: 15.609
Authors: Ian Smith; Peyton G Greenside; Ted Natoli; David L Lahr; David Wadden; Itay Tirosh; Rajiv Narayan; David E Root; Todd R Golub; Aravind Subramanian; John G Doench Journal: PLoS Biol Date: 2017-11-30 Impact factor: 8.029