Heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure.

ABSTRACT:

Background

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 possibl

SUBMITTER: Foroughi Pour A 

PROVIDER: S-EPMC5872553 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature