A pathway-based data integration framework for prediction of disease progression.

ABSTRACT:

Motivation

Within medical research there is an increasing trend toward deriving multiple types of data from the same individual. The most effective prognostic prediction methods should use all available data, as this maximizes the amount of information used. In this article, we consider a variety of learning strategies to boost prediction performance based on the use of all available data.

Implementation

We consider data integration via the use of multiple kernel learning supervised learning methods. We propose a scheme in which feature selection by statistical score is performed separately per data type and by pathway membership. We further consider the introduction of a confidence measure for the class assignment, both to remove some ambiguously labeled datapoints from the

SUBMITTER: Seoane JA 

PROVIDER: S-EPMC3957070 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature