I am a computer scientist and bioinformatician with over 15 years of experience in bioinformatics and computational methods. I graduated with a Ph.D. in Bioinformatics from the University of Hertfordshire (UK) in 2012). My initial research involved data mining big scale genetic and epigenetic data to identify novel genes and gene regulatory networks. I have extensive experience of genome-wide transcriptomic, genetic, and epigenetic analysis and next generation sequencing data analysis. Currently, my research interest involves in pattern recognition and machine learning for analysing, understanding and improving computational prediction of disease, phenotypes and functions relating to human health. I have track record of developing bioinformatics tools and pipelines, including: GEO import, Single Sample DNA Methylation Data Analysis. I 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).

I enjoy teaching and have over 12 years of experience. I am currently acting as the coordinator, lecturer and supervisor in undergraduate modules and projects at the Department of Computer Science. I am co-supervising two doctoral students. I am a Fellow of Higher Education Academy (FHEA) and has obtained the Postgraduate Certificate in Academic Practice.

Dr. Faisal I. Rezwan

Senior Lecturer in Bioinformatics
Department of Computer Science at Aberystwyth University.

Fellow of the Higher Education Academy (FHEA)

e-mail: f.rezwan at aber dot ac dot uk

ORCID id: 0000-0001-9921-222X

Latest news

  • Published: A Pregnancy and Childhood Epigenetics Consortium (PACE) meta-analysis highlights potential relationships between birth order and neonatal blood DNA methylation. Commun Biol.;7(1):66. doi: 10.1038/s42003-023-05698-x.(9 January 2024)
  • Published: Insights into the influence of diet and genetics on feed efficiency and meat production in sheep. Anim Genet.;55(1):20-46.doi: 10.1111/age.13383. (1 February 2024)
  • Published: Analysis of DNA methylation at birth and in childhood reveals changes associated with season of birth and latitude. Clin Epigenetics.;15(1):148. doi: 10.1186/s13148-023-01542-5. (11 September 2023)

Ongoing projects

Utilisation of recorded voice samples for developing a machine learning framework to predict pulmonary functions.We designed a threshold-based mechanism to separate speech and breathing from voice recordings in controlled environment and developed machine learning (ML) models which can predict lung function, severity of lung function abnormality and lung function abnormality.The findings from this project will not only address the challenge of handling voice recording data for appropriately separating speech and breathing to extract features to develop prediction models with high accuracy but also have potential in implementing as a smartphone application in the future, offering a convenient and straightforward way to assess respiratory health for individuals.

A 3D electrocardiogram (ECG) analyser model. We have developed a prototype software tool to visualise the electrical signature of heart produced from electrocardiogram (ECG) data. These new tool generates a summary description of the electrical signature in a numerical format for data analysis to produce initial 3D shapes of heart signals.This pilot project aims to develop robust software package which can produce a 3D visualisation of heart and integrate the functionality of diagnosing various heart conditions, with the use of 3D shape recognition, utilising machine learning, to mark the position of defect in the heart related to the condition.

Prediction model for pediatric asthma. Respiratory symptoms are common in early life and often transient. It is difficult to identify in which children these will persist and result in asthma. Machine learning (ML) approaches have the potential for better predictive performance and generalisability over existing childhood asthma prediction models. This study applied machine learning approaches to predict school-age asthma (age 10) in early life and at preschool age.

Pregnancy And Childhood Epigenetics (PACE). PACE consortium is comprised of researchers at NIEHS and around the world who are interested in studying the early life environmental impacts on human disease using epigenetics. Epigenetics refers to modifications to DNA that do not alter the DNA sequence.

Genetics of DNA Methylation (GoDMC). GoDMC was established with the view of bringing together researchers with an interest in studying the genetic basis of DNA methylation variation, to consolidate as many resources and expertise as possible and thereby expedite this field of research.To date, GoDMC comprises representatives from 50+ research groups. Together these groups have the potential to contribute data from multiple sources including a range of population, birth and disease specific cohorts, capturing a range of ages and ethnic backgrounds. Details on these cohorts can be found here .

Selected publications

I have published more than 60 peer-reviewed journal and conference papers. Following are some of my selected publications. The full list of publications can be found at Orcid.

Links to free open-access, preprints and e-prints are included where available. Some publisher links may be non-free.Presentation slides and other extra materials are sometimes included.


I am currently teaching in the following modules:

Plain Academic