Cancer classification: Mutual information, target network and strategies of therapy.

ABSTRACT:

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Background

Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy.

Methods

We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional inform

SUBMITTER: Hsu WC 

PROVIDER: S-EPMC3524788 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature