IFGFA: Identification of featured genes from genomic data using factor analysis

被引：0

作者：

Fu, C. H. ^{[1
,2
]}

Deng, S. ^{[2
]}

Wu, J. H. ^{[3
]}

Wu, X. Q. ^{[1
]}

Fu, Z. H. ^{[2
]}

Yu, Z. G. ^{[1
]}

机构：

[1] Xiangtan Univ, Sch Math & Computat Sci, Xiangtan, Peoples R China

[2] Shenyang Normal Univ, Sch Math & Syst Sci, Shenyang, Peoples R China

[3] Shenyang Normal Univ, Foreign Language Dept, Shenyang, Peoples R China

来源：

GENETICS AND MOLECULAR RESEARCH | 2016年 / 15卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Factor analysis; Featured gene; Gene expression profile; Multivariate statistics; SINGULAR-VALUE DECOMPOSITION; BREAST-CANCER; EXPRESSION; PREDICTION; CLASSIFICATION; SIGNATURES; DISCOVERY; NETWORKS; OUTCOMES; MODELS;

D O I：

10.4238/gmr.15038803

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

In this study, a software tool (IFGFA) for identification of featured genes from gene expression data based on latent factor analysis was developed. Despite the availability of computational methods and statistical models appropriate for analyzing special genomic data, IFGFA provides a platform for predicting colon cancer-related genes and can be applied to other cancer types. The computational framework behind IFGFA is based on the well-established Bayesian factor and regression model and prior knowledge about the gene from OMIM. We validated the predicted genes by analyzing somatic mutations in patients. An interface was developed to enable users to run the computational framework efficiently through visual programming. IFGFA is executable in a Windows system and does not require other dependent software packages.

引用

页数：8

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