Contact Dr Faisal Rezwan

Areas of expertise

  • Applied Informatics
  • Bioinformatics
  • Computing, Simulation & Modelling
  • Environment and Health

Background

Dr. Rezwan is a Lecturer in Bioinformatics in the School of Water, Energy and Environment. He is also an active member of the Bioinformatics Group in AgriFood. He has over 13 years of experience in the field of bioinformatics and computational methods. He graduated with a Ph.D. in Bioinformatics in 2012 and started his career as a research fellow in bioinformatics. His initial research involved data mining high throughput genetic and epigenetic data to identify novel genes and gene regulatory networks. He was involved in a Horizon2020 project (ALEC – Aging Lungs in European Cohorts). Dr. Rezwan was awarded the prestigious Turing fellowship and his research at Turing included a pilot project applying deep learning approach on patient data from Electronic Health Record Systems (EHRS). He has track record of developing bioinformatics tools and pipelines, such as: GEO import, Single Sample DNA Methylation Data Analysis. He has extensive experience of genome-wide transcriptomic, genetic, and epigenetic analysis and next generation sequencing data analysis.

Dr. Rezwan has over 10 years of experience in teaching. He is currently teaching and leading modules in the MSc in Applied Bioinformatics. He is also supervising two doctoral students. He is a Fellow of Higher Education Academy (FHEA) and has obtained the Postgraduate Certificate in Academic Practice.  

Current activities

Dr. Rezwan’s research mainly focuses on basic and applied problems in bioinformatics using data integration and data mining to analyse genetic and epigenetic data. His interest also involves in pattern recognition and machine learning for analysing, understanding and improving computational prediction for identifying genes, their regulatory regions and other biological features.


Data Integration: The main aim of the project is to develop a robust data integration technique for heterogeneous biological data sources, based on a meta-dimensional transformation based integration technique using a Bayesian Network approach, and further extend it by performing Differential Bayesian Network testing on networks, which will potentially differentiate phenotype. From the initial analysis, it has been found that Differential Bayesian Networks can efficiently construct separate networks using DNA methylation data from different populations.  This project would add value to both in fields of data integration and bioinformatics as it will develop a novel methodology for integrating omic datasets. Moreover, this project will contribute significantly to the computational modelling toolbox in life sciences as it can be potentially applied to other heterogeneous data sources.


Genetic interaction networks: This project utilised Deep Convolutional Neural Networks (DCNNs) to identify the complex relationship between genes and gene networks integrating genetic and epigenetic factors. Pair-wise associations (eQTL, mQTL, and eQTM) are identified between genotype, epigenotype, and gene expression. Subsequently separate DCNNs are generated with each type of the data, integrated together and further DCNNs model learned using the integrated data to make final predictions of key networks distinguishing specific phenotype of interest.

Clients

  • University of Southampton, UK
  • University of Memphis, USA
  • University of Bergen, Norway
  • Research Center Brostel, Germany

Publications

Articles In Journals