DrVahid Elyasi Gomari

Dr. Vahid Elyasigomari obtained his PhD from Queen Mary, University of London in the field of Medical Engineering and Bioinformatics. His PhD research presented an investigation into gene expression profiling using microarray and next generation sequencing (NGS) datasets, in relation to multi-category diseases such as cancer.

He then joined the Data Science Institute (DSI) at Imperial College London where he worked with a team to design a standards-compliant metadata framework for standardisation, integration, and harmonisation of translational medicine research data. Based on this framework, a new platform (PlatformTM) was developed which focuses on the management of translational research data assets throughout their different life-cycle stages. During this period he worked on projects such as European Translational Information and Knowledge Management Services (eTRIKS) and Biomarkers For Enhanced Vaccine Safety (BioVacSafe).

He is currently working in the Faculty of Medicine at Imperial College London (Jorge Ferrer Lab) on a project that aims to prioritise pathogenic non-coding enhancer mutations to help the genetic diagnosis of Maturity Onset Diabetes of the Young (MODY). He is eager to apply/develop machine learning methods that use various datasets (functional, comparative, and regulatory genomics features) to predict pathogenetic non-coding mutations that are potentially causal for MODY. Furthermore, he is working on developing novel methods for rare variant burden testing that overcome traditional approaches that are underpowered to detect the burden of ultra-rare mutations due to sample size.

His research interests are:

Bioinformatics and computational biology.
Machine learning in genetics and genomics
Rare diseases and enhancer mutations
Large scale data management and analysis.
Evolutionary algorithms for clustering and classification of multi-category disease.