NDLI: Heuristic Algorithms for Feature Selection under Bayesian Models with Block-diagonal Covariance Structure

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Heuristic Algorithms for Feature Selection under Bayesian Models with Block-diagonal Covariance Structure

Content Provider	ACM Digital Library
Author	Foroughi pour, Ali Dalton, Lori A.
Abstract	Many bioinformatics studies aim to identify markers, or features that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest, it may be necessary to consider combinations of features as a marker family. To this end, recent work proposes a hierarchical Bayesian framework for feature selection that places a prior on the set of features we wish to select and on the label-conditioned feature distribution. While an analytical posterior under Gaussian models with block covariance structures is available, the optimal feature selection algorithm for this model remains intractable since it requires evaluating the posterior over the space of all possible covariance block structures and feature-block assignments. To address this computational barrier, prior work proposes a simple suboptimal algorithm, 2MNC-Robust, with robust performance across the space of block structures. Here, we present three new heuristic feature selection algorithms that outperform 2MNC-Robust on synthetic data. Enrichment analysis on real cancer data indicates that they also output many of the genes and pathways linked to the cancers under study.
Starting Page	758
Ending Page	759
Page Count	2
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3110412
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Biomarker discovery Feature selection Bayesian learning Heuristic search algorithms
Content Type	Text
Resource Type	Article

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