Identification of biological markers in cancer disease using explainable artificial intelligence

Abstract

The research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimization technique to select specific genes as features. The model achieves high prediction accuracy with a Root Mean Square Error (RMSE) of 0.40 +/- 0.02 and R2 of 0.83 +/- 0.03 on the CCLE dataset, and an RMSE of 0.36 +/- 0.05 and R2 of 0.83 +/- 0.03 on the GDSC dataset. The approach also used an explainable artificial intelligence model to discover biological markers linked to cancer development. This can provide insights into targeted therapies for improving cancer treatment outcomes. Overall, the study presents an effective approach for identifying important biological markers relevant to cancer disease, aiding in the development of more efficient anticancer medications.