Two-steps learning of Fuzzy Cognitive Maps for prediction and knowledge discovery on the HIV-1 drug resistance

Abstract

The Human Immunodeficiency Virus (HIV) is a complex retrovirus that progressively deteriorates the immune system of infected patients, eventually causing death. Although antiviral drugs are not able to eradicate the HIV, they are designed to inhibit the function of three essential proteins in the virus replication process: protease, reverse transcriptase and integrase. However, due to a high mutation rate, this virus is capable to develop resistance to existing drugs causing the treatment failure. Several machine learning techniques have been proposed for predicting HIV drugs resistance, but most of them are difficult to interpret. Actually, in last years the protein modeling of this virus has become, from diverse points of view, an open problem for researchers. In this paper we propose a model based on Fuzzy Cognitive Maps (FCM) for analyzing the behavior of the HIV-1 protease protein. With this goal in mind, a two-steps learning algorithm using Swarm Intelligence for optimizing the modeling parameters is introduced. The first step is oriented to estimate the biological causality among amino acids describing the sequence through a continuous search method. While resulting adjusted maps are combined into a single one through an aggregation procedure for obtaining an initial prototype map. The second step optimizes the prototype by finding those amino acids directly associated with resistance using a discrete meta-heuristic. At the end, a fully optimized prototype map is obtained allowing predicting HIV-1 drug resistance and also discovering relevant knowledge on causal influences directly associated with resistance, for seven well-known protease inhibitors.