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Bioinformatics. 2014 Mar 15;30(6):838-45. doi: 10.1093/bioinformatics/btt610. Epub 2013 Oct 24.

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

Author information

1
MRC Centre for Causal Analyses in Translational Epidemiology, MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Clifton BS8 2BN, UK and Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, UK.

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 training data and to implement a cautious classifier that only makes predictions when the associated confidence is high.

RESULTS:

We use the METABRIC dataset for breast cancer, with prediction of survival at 2000 days from diagnosis. Predictive accuracy is improved by using kernels that exclusively use those genes, as features, which are known members of particular pathways. We show that yet further improvements can be made by using a range of additional kernels based on clinical covariates such as Estrogen Receptor (ER) status. Using this range of measures to improve prediction performance, we show that the test accuracy on new instances is nearly 80%, though predictions are only made on 69.2% of the patient cohort.

AVAILABILITY:

https://github.com/jseoane/FSMKL CONTACT: J.Seoane@bristol.ac.uk

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
24162466
PMCID:
PMC3957070
DOI:
10.1093/bioinformatics/btt610
[Indexed for MEDLINE]
Free PMC Article

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