Fuzzy Cognitive Maps for Modelling, Predicting and Interpreting HIV Drug Resistance

Isel Grau*, Gonzalo Nápoles, Maikel León, Ricardo Grau

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


The high mutability of Human Immunodeficiency Virus (HIV) leads to serious problems on designing efficient antiviral drugs. In fact, in last years the study of drug resistance prediction for HIV mutations has become an open problem for researchers. Several machine learning techniques have been proposed for modelling this sequence classification problem, but most of them are difficult to interpret. This paper presents a modelling of the protease protein as a dynamic system through Fuzzy Cognitive Maps, using the amino acid contact energies for the sequence description. In addition, a Particle Swarm Optimization based learning scheme called PSO-RSVN is used to estimate the causal weight matrix that characterize these structures. Finally, a study with statistical techniques for knowledge discovery is conducted, for determining patterns in the causal influences of each sequence position on the resistance to five well-known inhibitor drugs.
Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence
Number of pages10
Publication statusPublished - 2012
Externally publishedYes
Event13th Ibero-American Conference on Artificial Intelligence -
Duration: 13 Nov 2012 → …


Conference13th Ibero-American Conference on Artificial Intelligence
Period13/11/12 → …


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