A Robust Random Forest Prediction Model for Mother-to-Child HIV Transmission Based on Individual Medical History

Human Immunodeficiency Virus (HIV) continues to be a leading cause of mortality and reduces manpower throughout the world. HIV transmission from mother to child is still a global challenge in health research. According to UNAIDS, in every 7 girls, 6 are found to be newly infected among adolescents whereby 15-24 years are likely to be living with HIV which is the maternal age and likely to transfer to the child. Machine learning methods have been used to predict HIV/AIDS transmission from mother to child but left behind some important considerations including the use of patient-level information and techniques in balancing the dataset which may impact models’ performance. A robust prediction model for mother-to-child HIV/AIDS transmission is vital to alleviate HIV/AIDS detrimental effects. The Random Forest Machine Learning method was employed based on features from the individual medical history of HIV-positive mothers. A total of 680 balanced data tuples were used for model development using the ratio of 75:25 for training and testing the dataset. The Random Forest model outperformed the most commonly used learning algorithms achieving the performance of 99% accuracy, recall and F1-score of 0.99 and an error of 0.01, thus improving the prediction rate

HIV Resistance Prediction using Feed Forward Neural Networks and Sequence Expansion Methodologies

HIV is a chronic and debilitating disease affecting the lives of millions of people globally. While therapies to treat HIV are available, drug resistance is a consistent problem. For this reason, an effective means of determining drug resistance for a given isolate is needed. In this experiment, we use a simple Artificial Neural Network (ANN) model trained on phenotypically labeled sequences from HIVdb for resistance classifications. We also observe an interesting data processing method, and determine train and test set division before such data processing is optimal for network performance

HIV drug resistance prediction with weighted categorical kernel functions

Background: Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug resistance to previously unobserved variants is therefore very important for an optimum medical treatment. In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance. Results: We analyzed 21 drugs of four classes: protease inhibitors (PI), integrase inhibitors (INI), nucleoside reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI). We compared two categorical kernel functions, Overlap and Jaccard, against two well-known noncategorical kernel functions (Linear and RBF) and Random Forest (RF). Weighted versions of these kernels were also considered, where the weights were obtained from the RF decrease in node impurity. The Jaccard kernel was the best method, either in its weighted or unweighted form, for 20 out of the 21 drugs. Conclusions: Results show that kernels that take into account both the categorical nature of the data and the presence of mixtures consistently result in the best prediction model. The advantage of including weights depended on the protein targeted by the drug. In the case of reverse transcriptase, weights based in the relative importance of each position clearly increased the prediction performance, while the improvement in the protease was much smaller. This seems to be related to the distribution of weights, as measured by the Gini index. All methods described, together with documentation and examples, are freely available at https://bitbucket.org/elies_ramon/catkern.Peer ReviewedPostprint (published version

A HIV/AIDS viral load prediction system using artificial neural networks

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityHuman Immunodeficiency Virus (HIV) has been affecting people since it was first discovered in 1986. This is as a result of the HIV virus being present in the patient bloodstream for the remainder of their normal life, as there is no cure that exists as of now. HIV, if left unmanaged would end up developing into Acquired Immune Deficiency Syndrome (AIDS), a syndrome that weakens a patient’s immune system and leaves them susceptible to other opportunistic infections. Antiretroviral therapy (ART) has been successfully used in managing the progression of the HIV virus in the human body. However, poor adherence attributable to ignorance, adverse drug effects, and age have derailed the attainment of viral load suppression amongst the HIV positive people. The progression of the virus is tracked by counting Cluster of Differentiation 4 positive cells, and the amount of virus in the blood (viral load) every 6 months. This research introduces the use of multi-layer artificial neural networks with backpropagation to predict the HIV/AIDS viral load levels over a given period of time (in weeks). The Data-driven Modelling methodology was used in the development of the model. This methodology was ideal since the model relied solely on pre-existing data, and supports artificial neural networks. The model developed performed at an accuracy level of 93.76% and a mean square error of 0.0323. The results showed that the neural network can be used as a suitable algorithm for HIV/AIDS viral load level prediction. The learning rate used in the study was 0.005 and the momentum was 0.9. The iterations for the training, testing and validation varied

Bioinformatics Techniques for Studying Drug Resistance In HIV and Staphylococcus Aureus

The worldwide HIV/AIDS pandemic has been partly controlled and treated by antivirals targeting HIV protease, integrase and reverse transcriptase, however, drug resistance has become a serious problem. HIV-1 drug resistance to protease inhibitors evolves by mutations in the PR gene. The resistance mutations can alter protease catalytic activity, inhibitor binding, and stability. Different machine learning algorithms (restricted boltzmann machines, clustering, etc.) have been shown to be effective machine learning tools for classification of genomic and resistance data. Application of restricted boltzmann machine produced highly accurate and robust classification of HIV protease resistance. They can also be used to compare resistance profiles of different protease inhibitors. HIV drug resistance has also been studied by enzyme kinetics and X-ray crystallography. Triple mutant HIV-1 protease with resistance mutations V32I, I47V and V82I has been used as a model for the active site of HIV-2 protease. The effects of four investigational antiviral inhibitors was measured for Triple mutant. The tested compounds had significantly worse inhibition of triple mutant with Ki values of 17-40 nM compared to 2-10 pM for wild type protease. The crystal structure of triple mutant in complex with GRL01111 was solved and showed few changes in protease interactions with inhibitor. These new inhibitors are not expected to be effective for HIV-2 protease or HIV-1 protease with changes V32I, I47V and V82I. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that causes hospital and community-acquired infections. Antibiotic resistance occurs because of newly acquired low-affinity penicillin-binding protein (PBP2a). Transcriptome analysis was performed to determine how MuM (mutated PBP2 gene) responds to spermine and how Mu50 (wild type) responds to spermine and spermine–β-lactam synergy. Exogenous spermine and oxacillin were found to alter some significant gene expression patterns with major biochemical pathways (iron, sigB regulon) in MRSA with mutant PBP2 protein

The Structural Basis for the Interdependence of Drug Resistance in the HIV-1 Protease

The human immunodeficiency virus type 1 (HIV-1) protease (PR) is a critical drug target as it is responsible for virion maturation. Mutations within the active site (1°) of the PR directly interfere with inhibitor binding while mutations distal to the active site (2°) to restore enzymatic fitness. Increasing mutation number is not directly proportional to the severity of resistance, suggesting that resistance is not simply additive but that it is interdependent. The interdependency of both primary and secondary mutations to drive protease inhibitor (PI) resistance is grossly understudied. To structurally and dynamically characterize the direct role of secondary mutations in drug resistance, I selected a panel of single-site mutant protease crystal structures complexed with the PI darunavir (DRV). From these studies, I developed a network hypothesis that explains how mutations outside the active site are able to perpetuate changes to the active site of the protease to disrupt inhibitor binding. I then expanded the panel to include highly mutated multi-drug resistant variants. To elucidate the interdependency between primary and secondary mutations I used statistical and machine-learning techniques to determine which specific mutations underlie the perturbations of key inter-molecular interactions. From these studies, I have determined that mutations distal to the active site are able to perturb the global PR hydrogen bonding patterns, while primary and secondary mutations cooperatively perturb hydrophobic contacts between the PR and DRV. Discerning and exploiting the mechanisms that underlie drug resistance in viral targets could proactively ameliorate both current treatment and inhibitor design for HIV-1 targets

The HIV-1 gag and protease: exploring the coevolving nature and structural implications of complex drug resistance mutational patterns in subtype C.

Doctoral Degree. University of KwaZulu-Natal, Durban.Due to the high prevalence of HIV-1 subtype C infection coupled with increasing antiretroviral (ARV) drug treatment failure, the elucidation of complex resistance mutational patterns arsing through protein coevolution is required. Despite the inclusion of LPV and DRV in second- and third-line, many patients still fail treatment. In this study, protease (PR) inhibitor resistance mutations were identified by comparing treatment versus naïve sequences datasets in Gag and PR. Thereafter, to investigate Gag-PR coevolution and pathways to LPV resistance, phylogenetic analyses and Bayesian networks were constructed. Following this, structural analyses combining homology modelling, molecular docking and molecular dynamic simulations were carried out on specific patterns of protease resistance mutations (PRMs). To complement these analyses, the structural impact of a mutated Gag cleavage site on PR resistance dynamics was also evaluated. Accordingly, this study identified 12 major PRMs and several resistance combinations. Of these, the M46I+I54V+V82A pattern frequently occurred. The second most frequently recurring pattern included L76V as a fourth mutation to the above triplet. Coevolution analyses revealed correlations between positions 10, 46, 54 and 82 in PR. Of these, minor PRM L10F occurred in 6.4% of the dataset and was involved in pathways to LPV resistance. Additionally, Gag cleavage site (CS) mutation A431V was also correlated with L10F and the major PRMs. Distinct changes in PR’s active site, flap and elbow regions due to the PRMs (L10F, M46I, I54V, L76V, V82A) were found to alter LPV and DRV drug binding. When the PRMs were combined with the mutant Gag CS binding was greatly exacerbated. While the A431V Gag CS mutation coordinated several amino acid residues in PR, the L76V mutation was found to have a significant role in substrate recognition rather than directly inhibiting the drugs. These data show that the co-selection of mutations in Gag-PR greatly contributes to resistance outcomes and that our understanding on drug resistance is largely lacking, particularly where structure is concerned.Conference Presentations can be found on page iv of this thesis