MLCapsNet +: A multi-capsule network for the identification of the HIV ISs along important sequence positions

Abstract

The most studied sub-category of the retrovirus is the human immunodeficiency virus (HIV), which is a type of virus of the Retroviridae family. The HIV integration site (HIV IS)/ integration sites (HIV ISs) denote a crucial entity in the entire process of infection and its rebound if there is an interruption in therapy. It determines the steps involved in the formation of latent viral reserve. This work proposes a very deep neural network framework, where each of the layers of the multi-layered network is itself a neural network (NN). The attention mechanism is used for the extraction of the importance of positions in terms of an attention map. This framework will use the Capsule Network along with the attention mechanism to increase the explainability about the presence of local features. Convolutional neural networks (CNNs), which are specialized for image-based recognition and classification, have many drawbacks with one of these being its invariance to translation. This drawback has been overcome by the Capsule Networks. The proposed model also identifies the HIV ISs achieving a performance better than the State-of-the-art methods. Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR) classifiers have been used. Only two state-of-the-art methods are present in the literature that achieve these two goals and a comparison of this work with those two works has been provided here. This work performs way better than the state-of-the-art work in detecting the HIV IS. The comparison is presented here in terms of AUC-ROC, AUC-PR, accuracy, confusion matrix, and Fβ score.