Mining the Viability Profiles of Different Breast Cancer: A Soft Computing Perspective

Abstract

Cancer cells present several mutations that allow them to grow faster than normal cells, at the time that enables them to avoid apotosis and other control processes. Cancer cell may be affected by synthetic lethality, which refers to the induction of one or more mutations that affect them, but affect normal cells as little as possible. It is one of the goals of bioinformatics to identify synthetic mutations in order to target specific cancers. If synthetic mutations affect several cancer cells, then it is possible that also some normal cells may be affected. In this contribution, we describe a methodology able to identify a small set of those mutations that affect in a differential way several breast cancer lines. Our methodology is an instance of the feature selection problem and based in genetic algorithms for the exploration of the solution space, but guided by mutual information. Our results show that cancer lines can be profiled with only a small subset of mutations from an original list of hundreds of mutations.