Feature Selection to Predict Compound's Effect on Aging

Abstract

Biological aging process is the main cause to many age-related diseases. Therefore, exploring cellular level changes due to aging, chemical impacts and anti-aging compounds are of high interest in drug discovery and personalized drugs research. In this paper, we propose a model to predict the effect of chemical compounds on lifespan of Caenorhabditis elegans. We analyze the data from DrugAge database, which includes chemical compounds that affect lifespan of model organisms and use chemical descriptors and gene ontology as features. We propose a new feature selection scheme based on particle swarm optimization and correlation-based feature selection to select the most relevant features for classification task. The experimental results indicate our approach achieves higher performance over the existing methods. We discuss the benefits of our proposed feature selection schema over other methodologies and compare our results conducted by random forest with base-line support vector machine and artificial neural network classifiers.