Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling

• Published in: Cells (Basel, Switzerland) Switzerland), 2023-02, Vol.12 (5), p.772
• Main Authors: Altamirano-Flores, José Salomón, Alvarado-Hernández, Luis Ángel, Cuevas-Tello, Juan Carlos, Tino, Peter, Guerra-Palomares, Sandra E, Garcia-Sepulveda, Christian A
• Format: Article
• Language: English
• Subjects: Acquired immune deficiency syndrome; AIDS; Amino Acid Motifs; Bayes Theorem; CD4 antigen; Datasets; Enzymes; Epidemiology; Genetic algorithms; Genetic aspects; Genetic diversity; Genomes; Health aspects; HIV; HIV (Viruses); HIV Infections; HIV-1 - metabolism; HIV-Vif; Human immunodeficiency virus; Humans; Identification and classification; Immune system; Infectivity; Learning algorithms; Lymphocytes; Lymphocytes T; Machine Learning; Mutation; Mutation (Biology); Neural networks; Proteins; Statistical analysis; undersampling; Variables; vif Gene Products, Human Immunodeficiency Virus - genetics; vif Gene Products, Human Immunodeficiency Virus - metabolism; Vif protein; Viral proteins
• ISSN: 2073-4409; 2073-4409
• DOI: 10.3390/cells12050772

Human Immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Although great advances have been made towards determining the way viral genetic diversity affects clinical outcome, genetic association studies have been hindered by the complexity of their interactions with the human host. This study provides an innovative approach for the identification and analysis of epidemiological associations between HIV Viral Infectivity Factor (Vif) protein mutations and four clinical endpoints (Viral load and CD4 T cell numbers at time of both clinical debut and on historical follow-up of patients. Furthermore, this study highlights an alternative approach to the analysis of imbalanced datasets, where patients without specific mutations outnumber those with mutations. Imbalanced datasets are still a challenge hindering the development of classification algorithms through machine learning. This research deals with Decision Trees, Naïve Bayes (NB), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs). This paper proposes a new methodology considering an undersampling approach to deal with imbalanced datasets and introduces two novel and differing approaches (MAREV-1 and MAREV-2). As theses approaches do not involve human pre-determined and hypothesis-driven combinations of motifs having functional or clinical relevance, they provide a unique opportunity to discover novel complex motif combinations of interest. Moreover, the motif combinations found can be analyzed through traditional statistical approaches avoiding statistical corrections for multiple tests.