Discovering combinatorial interactions in survival data.

ABSTRACT:

Motivation

Although several methods exist to relate high-dimensional gene expression data to various clinical phenotypes, finding combinations of features in such input remains a challenge, particularly when fitting complex statistical models such as those used for survival studies.

Results

Our proposed method builds on existing 'regularization path-following' techniques to produce regression models that can extract arbitrarily complex patterns of input features (such as gene combinations) from large-scale data that relate to a known clinical outcome. Through the use of the data's structure and itemset mining techniques, we are able to avoid combinatorial complexity issues typically encountered with such methods, and our algorithm performs in similar orders of duration as si

SUBMITTER: Duverle DA 

PROVIDER: S-EPMC3834797 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature