Support vector machine algorithms in the search of KIR gene associations with disease

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

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.

KIR Genes and Patterns Given by the A Priori Algorithm: Immunity for Haematological Malignancies

Killer-cell immunoglobulin-like receptors (KIRs) are membrane proteins expressed by cells of innate and adaptive immunity. The KIR system consists of 17 genes and 614 alleles arranged into different haplotypes. KIR genes modulate susceptibility to haematological malignancies, viral infections, and autoimmune diseases. Molecular epidemiology studies rely on traditional statistical methods to identify associations between KIR genes and disease. We have previously described our results by applying support vector machines to identify associations between KIR genes and disease. However, rules specifying which haplotypes are associated with greater susceptibility to malignancies are lacking. Here we present the results of our investigation into the rules governing haematological malignancy susceptibility. We have studied the different haplotypic combinations of 17 KIR genes in 300 healthy individuals and 43 patients with haematological malignancies (25 with leukaemia and 18 with lymphomas). We compare two machine learning algorithms against traditional statistical analysis and show that the “a priori” algorithm is capable of discovering patterns unrevealed by previous algorithms and statistical approaches.

Gene network biological validity based on gene-gene interaction relevance

In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.