Adjustment of p-value expression to ontology using machine learning for genetic prediction, prioritization, interaction, and its validation in glomerular disease - Ettetuani, Boutaina; Chahboune, Rajaa; Moussa, Ahmed (Frontiers in Genetics, 2023)
The results of gene expression analysis based on p-value can be extracted and sorted by their absolute statistical significance and then applied to multiple similarity scores of their gene ontology (GO) terms in furtherance of combining and adjusting these scores as essential prediction tasks for understanding biological/clinical pathways. These last enable the possibility to assess whether particular aspects of gene function might be associated with other varieties of genes, to evaluate the regulation and link them into networks that prioritize candidate genes as classification by applying machine learning techniques. Then we detect significant genetic interactions based on our algorithm to validate the results. Finally, based on specifically selected tissues according to their normalized gene expression and frequencies of occurrence from their different biological and clinical input, a reported classification of genes among the subject category has validated the abstracts (glomerular diseases) as a case study.