Abstract
The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between “black box” deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data.
Highlights
Human immunodeficiency virus (HIV), which causes acquired immunodeficiency syndrome (AIDS), affects over 1.1 million people in the U.S today [1]
We compared the performance of three deep learning architectures for binary classification of HIV sequences by drug resistance: multilayer perceptron (MLP), bidirectional recurrent neural network (BRNN), and convolutional neural network (CNN) (Table 2)
Due to the noted class imbalances in the data, accuracy is not an ideal metric to compare performance, so we considered area-under-the-receiver operating characteristic curve (AUC) and the F1 score, both of which are more appropriate in this case
Summary
Human immunodeficiency virus (HIV), which causes acquired immunodeficiency syndrome (AIDS), affects over 1.1 million people in the U.S today [1]. Consistent treatment with ART can extend the life expectancy of an HIV-positive individual to nearly as long as that of a person without HIV by reducing the viral load to below detectable levels [2] and can reduce transmission rates [3,4]. The fast replication rate and lack of repair mechanisms of HIV leads to a large number of mutations, many of which result in the evolution of HIV to resist antiretroviral drugs [5,6]. Drug resistance may be conferred at the time of HIV transmission, so even treatment-naïve patients may be resistant to certain ART drugs, which can lead to rapid drug failure [7]. The analysis of drug resistance is critical to treating HIV, and is an important focus of HIV research
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