BACKGROUND: Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these a...
PURPOSE: Next generation sequencing has led to the creation of large pools of genomic data with analysis rather than data generation now the limiting factor. Artificial intelligence (AI) may be required to optimize the benefits of these data, but lit...
Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. L...
We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence align...
BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with rec...
BACKGROUND: This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have t...
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, arti...
Identification of genomic signals as indicators for functional genomic elements is one of the areas that received early and widespread application of machine learning methods. With time, the methods applied grew in variety and generally exhibited a t...
BACKGROUND: Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data i...
In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed ...