Modeling association detection in order to discover compounds to inhibit oral cancer.

Abstract

In the past, algorithms exploiting varying semantics in interactions between biological objects such as genes and diseases have been used in bioinformatics to uncover latent relationships within biological datasets. In this paper, we consider the algorithm Medusa in parallel with binary classification in order to find potential compounds to inhibit oral cancer. Oral cancer affects the mouth and pharynx and has a high mortality rate due to its late discovery. Current methods of oral cancer treatment, such as chemoradiation and surgery, fail to provide better chances for survival, warranting an alternative approach. By running Medusa on a data fusion graph consisting of biological objects, we incorporate binary classification to model the algorithm's association detection to discover compounds with the potential to mitigate the effects of oral cancer.