Summary
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.
Publications
Journals
Mumby S, Perros F, Hui C, et al., 2021, Extracellular matrix degradation pathways and fatty acid metabolism regulate distinct pulmonary vascular cell types in Pulmonary Arterial Hypertension, Pulmonary Circulation, Vol:11, ISSN:2045-8940, Pages:1-16
Emam I, Elyasigomari V, Matthews A, et al., 2019, PlatformTM, a standards-based data custodianship platform for translational medicine research., Scientific Data, Vol:6, ISSN:2052-4463, Pages:149-149
Pavlidis S, Takahashi K, Kwong FNK, et al., 2019, "T2-high" in severe asthma related to blood eosinophil, exhaled nitric oxide and serum periostin, European Respiratory Journal, Vol:53, ISSN:0903-1936
Elyasigomari V, Lee DA, Screen HRC, et al., 2017, Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification., J Biomed Inform, Vol:67, Pages:11-20
Conference
Mumby S, Elyasigomari V, Hui CK, et al., 2019, Evidence for Endothelial Barrier Dysfunction, Vascular Permeability and Altered Matrix Degradation in PAH Pathogenesis Using RNA-Sequence Analysis, International Conference of the American-Thoracic-Society, AMER THORACIC SOC, ISSN:1073-449X