Developing MLP based prediction system for anticancer drug response using hybrid features of genomics and cheminformati
Awais Raza Zaidi · Lahore Garrison University Research Journal of Computer Science and Information Technology · 2024
Traditional cancer treatment methods have become less effective due to the increasing diversityof cancer types. To address this, precision medicine has gained support within the medical community.This approach tailors treatment to individual patients based on their specific disease characteristics.However, a major challenge lies in accurately predicting how a patient will respond to a specializeddrug. Numerous machine learning-based predictive systems have been developed to address thischallenge. These systems utilize genomic signatures and the chemical structure of drugs to predictdrug activity. In this paper, we introduce a Multi-Layer Perceptron (MLP) based system for predictingthe response of anticancer drugs. Our system utilizes hybrid features derived from both genetic expressionand the chemical structure of drugs. It is developed using the well-known GDSC dataset (Genomicsof Drug Sensitivity in Cancer). Our system achieved a lower Root Mean Square Error (RMSE)value of 0.889, in contrast to the RMSE value of 0.983 obtained by the current state-of-the-art (SOTA)system, SwNet. This indicates superior predictive accuracy. The findings suggest that our proposedresearch holds promise for the development of targeted drugs for anticancer treatments.