Predicting Human Immunodeficiency Virus (HIV) Drug Resistance using Recurrent Neural Networks

Motivation: Predicting HIV resistance to drugs is one of many problems for which bioinformaticians have implemented and trained machine learning methods, such as neural networks. Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Furthermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network.
Results: In this paper, we demonstrate that the amino acid energies are good features to represent the HIV genotype. In addition, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this problem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. The accuracies vary between 81.3% and 94.7%.