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) where he has been working on projects such as European Translational Information and Knowledge Management Services (eTRIKS) and Biomarkers For Enhanced Vaccine Safety (BioVacSafe). He currently works with a team at the DSI to develop a translational medicine data management platform (PlatformTM).
His research interests are:
- Bioinformatics and computational biology.
- Large scale data management and analysis.
- Medical electronics and physics.
- Evolutionary algorithms for clustering and classification of multi-category disease.
et al., 2017, Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckcio optimization algorithm and harmony search for cancer classification, Journal of Biomedical Informatics, Vol:67, ISSN:1532-0464, Pages:11-20
et al., 2016, Evaluation of the detrimental effects in osmotic power assisted reverse osmosis (RO) desalination, Renewable Energy, Vol:93, ISSN:0960-1481, Pages:608-619
et al., 2015, Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization, Applied Soft Computing, Vol:35, ISSN:1568-4946, Pages:43-51