Multiple Sclerosis Biomarker Discovery via Bayesian Feature Selection

被引：7

作者：

Pour, Ali Foroughi ^{[1
]}

Dalton, Lori A. ^{[1
]}

机构：

[1] Ohio State Univ, 2015 Neil Ave, Columbus, OH 43210 USA

来源：

PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS | 2016年

关键词：

Feature Selection; Biomarker Discovery; Multiple Sclerosis;

D O I：

10.1145/2975167.2985680

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Recent work proposes a hierarchical Bayesian framework for feature selection, where a prior describes the identity of each feature set and the underlying distribution parameters. Assuming jointly Gaussian features, a posterior is found in closed form, and an approximation is presented to develop fast suboptimal algorithms. Applying this method to multiple sclerosis data we find highly ranked genes and pathways suggested to be involved in multiple sclerosis.

引用

页码：540 / 541

页数：2

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