A multitask transfer learning framework for novel virus-human protein interactions

Abstract

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection. This could further help in developing treatments of viral diseases. The main issues in tackling it as a machine learning problem is the scarcity of training data as well input information of the viral proteins. We overcome these limitations by exploiting powerful statistical protein representations derived from a corpus of around 24 Million protein sequences in a multi task framework. Our experiments on 7 varied benchmark datasets support the superiority of our approach.