Collaborative Mining of Whole Genome Sequences for Intelligent HIV-1 Sub-Strain(s) Discovery

• Published in: Current HIV Research
• Main Authors: Ekpenyong, Moses E., Adegoke, Anthony A., Edoho, Mercy E., Inyang, Udoinyang G., Udo, Ifiok J., Ekaidem, Itemobong S., Osang, Francis, Uto, Nseobong P., Geoffery, Joseph I.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Bentham Science Publishers Ltd. 2022
• Subjects: Virology; Infectious Diseases; Antarc*; Antarctica
• Online Access: http://dx.doi.org/10.2174/1570162x20666220210142209; https://www.eurekaselect.com/article/download?doi=10.2174/1570162X20666220210142209; https://www.eurekaselect.com/201020/article

Background: Effective global antiretroviral vaccines and therapeutic strategies depend on the diversity, evolution, and epidemiology of their various strains as well as their transmission and pathogenesis. Most viral disease-causing particles are clustered into a taxonomy of subtypes to suggest pointers toward nucleotide-specific vaccines or therapeutic applications of clinical significance sufficient for sequence-specific diagnosis and homologous viral studies. These are very useful to formulate predictors to induce cross-resistance to some retroviral control drugs being used across study areas. Objective: This research proposed a collaborative framework of hybridized (Machine Learning and Natural Language Processing) techniques to discover hidden genome patterns and feature predictors for HIV-1 genome sequences mining. Method: 630 human HIV-1 genome sequences above 8500 bps were excavated from the National Center for Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov) for 21 countries across different continents, except for Antarctica. These sequences were transformed and learned using a self-organizing map (SOM). To discriminate emerging/new sub-strain(s), the HIV-1 reference genome was included as part of the input isolates/samples during the training. After training the SOM, component planes defining pattern clusters of the input datasets were generated for cognitive knowledge mining and subsequent labeling of the datasets. Additional genome features, including dinucleotide transmission recurrences, codon recurrences, and mutation recurrences, were finally extracted from the raw genomes to construct output classification targets for supervised learning. Results: SOM training explains the inherent pattern diversity of HIV-1 genomes as well as interand intra-country transmissions in which mobility might play an active role, as corroborated by the literature. Nine sub-strains were discovered after disassembling the SOM correlation hunting matrix space attributed to disparate clusters. ...