Muhammad Asif

The paper identifies 70 miRNAs, including novel candidates, involved in ASD by analyzing genomic variants and their regulatory networks.

ShIVA provides a user-friendly interface for biologists to analyze single-cell RNA-seq and CITE-seq data using Seurat v4, enabling reproducible analysis and visualization.

The paper presents a KNIME workflow for scRNA-seq analysis that is customizable, interactive, and automated, enabling users without programming expertise to perform key analysis steps.

DGH-GO is an application that uses Gene Ontology to cluster disease-causing genes and identify genetic heterogeneity in complex diseases like ASD and NDDs.

This study uses machine learning to identify biological mechanisms underlying ASD subgroups based on clinical and genetic data, finding distinct processes in mild and severe cases.

This paper pilots the adaptation of the ELEAT tool in Portugal to assess environmental exposures in children with ASD, aiming to identify modifiable risk factors for the disorder.

This study identifies genes involved in detoxification and barrier permeability with variants linked to ASD and discovers gene-environment interactions relevant to the disorder.

This study uses machine learning with gene functional similarities to identify novel ASD genes, achieving higher accuracy than existing methods.