中国科学院重庆绿色智能技术研究院机构知识库

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	Prioritizing predictive biomarkers for gene essentiality in cancer cells with mRNA expression data and DNA copy number profile
	Guan, Yuanfang1; Li, Tingyang1; Zhang, Hongjiu1; Zhu, Fan2; Omenn, Gilbert S.1,3,4,5
	2018-12-01
摘要	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.
DOI	10.1093/bioinformatics/bty467
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	34期号:23页码:3975-3982
通讯作者	Guan, Yuanfang(gyuanfan@umich.edu) ; Omenn, Gilbert S.()
收录类别	SCI
WOS记录号	WOS:000453453300003
语种	英语