Publication:
A multitask multiple kernel learning algorithm for survival analysis with application to cancer biology

Placeholder

School / College / Institute

Organizational Unit

Program

KU Authors

Co-Authors

Publication Date

Language

Embargo Status

Journal Title

Journal ISSN

Volume Title

Alternative Title

Abstract

Predictive performance of machine learning algorithms on related problems can be improved using multitask learning approaches. Rather than performing survival analysis on each data set to predict survival times of cancer patients, we developed a novel multitask approach based on multiple kernel learning (MKL). Our multitask MKL algorithm both works on multiple cancer data sets and integrates cancer-related pathways/gene sets into survival analysis. We tested our algorithm, which is named as Path2MSurv, on the Cancer Genome Atlas data sets analyzing gene expression profiles of 7,655 patients from 20 cancer types together with cancer-specific pathway/gene set collections. Path2MSury obtained better or comparable predictive performance when benchmarked against random survival forest, survival support vector machine, and single-task variant of our algorithm. Path2MSury has the ability to identify key pathways/gene sets in predicting survival times of patients from different cancer types.

Source

Publisher

JMLR-Journal Machine Learning Research

Subject

Computer science, Artificial intelligence

Citation

Has Part

Source

International Conference on Machine Learning, Vol 97

Book Series Title

Edition

DOI

item.page.datauri

Link

Rights

Copyrights Note

Endorsement

Review

Supplemented By

Referenced By

0

Views

0

Downloads