Developing Optimally Discriminative Subnetwork Markers for Predicting Response to Chemotherapy

Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein–protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems.

Faculty Supervisor:

Dr. Cenk Sahinalp

Student:

Yen-Yi Lin

Partner:

Vancouver Prostate Centre

Discipline:

Computer science

Sector:

Life sciences

University:

Simon Fraser University