Tameera Rahman

My interests lie within applied multidisciplinary science and research. With a first degree in Chemistry, I started my career as a technical writer and then moved into multidisciplinary research with an MSc in Computational Biology and then a PhD in Computer Science.

Here at HDI working as a Data Scientist, I currently manage the data on CRUK funded projects CORECT-R and CanGeneCanVar(Work Package 1) in collaboration with Professor Eva Morris from the University of Oxford. I also work on a couple of audits, Post Colonoscopy ColoRectal Cancer (PCCRC) and Post Endoscopy Upper GastroIntestinal Cancer (PEUGIC).

Out of work, I love my daily morning run. Only just discovered it’s more fun with audible and gradually easing my way through some ‘must-reads’ collecting over months of post-house-move DIY..

Tameera Rahman

Health Data Scientist


Tameera has a first degree in Chemistry with Biotechnology and started her career as a technical writer for a tech company in India. Keen to pursue her interest in interdisciplinary research she studied for a masters by research in computational biology where she researched maximum likelihood and neighbour joining methods for building phylogenetic trees for antibiotic producing bacteria.  During her PhD she used machine learning techniques to design in-silico models to predict viral antigenic variability in viruses to expedite the vaccine design process. This involved normalising large genomic and serological datasets before statistically analysing them to look for patterns/relationships to identify variables for mathematical models.

Here at HDI, Tameera manages the data on the colorectal cancer intelligence hub, which aims to bring together datasets that are relevant to colorectal cancer in the UK to form a single accessible cancer data repository (CORECT-R) to ultimately drive improvements in colorectal cancer outcomes. Her role involves generating data in yearly schemas, extracting data sets for the clinical teams in the wider CORECT-R project, ensuring the integrity and security of the data, reporting on, and updating the relevant metadata.

On the CanGeneCanVar (WP1) project that aims to utilise nationally available cancer datasets for evaluating risks relating to cancer susceptibility genes; she undertakes scoping, specifying, developing, and managing data used by the various studies while ensuring sensitive data is handled and shared securely both internally and with external stakeholders.

On PCCRC and PEUGIC projects, she is involved in specifying and preparing the data for the national audits.

Education and Awards:

PhD in Computer Science – 2017
University of Surrey, Guildford, United Kingdom

MSc in Computational Biology (Distinction) – 2012
University of East Anglia, Norwich, United Kingdom

MA in Media Management (Distinction) – 2008University of Westminster, London, United Kingdom

BSc in Chemistry and Biotechnology (First) – 2006
Bangalore University, Bangalore, India


Allen, Isaac & Rahman, Tameera & Bacon, Andrew & Knott, Craig & Jose, Sophie & Vernon, Sally & Hassan, Hend & Huntley, Catherine & Loong, Lucy & Walburga, Yvonne & Lavelle, Katrina & Morris, Eva & Hardy, Steven & Torr, Beth & Eccles, Diana & Turnbull, Clare & Tischkowitz, Marc & Pharoah, Paul & Antoniou, Antonis. (2023). Abstract 3057: Second primary cancer risks for female and male breast cancer survivors. Cancer Research. 83. 3057-3057. 10.1158/1538-7445.AM2023-3057.

Hassan, Hend & Rahman, Tameera & Bacon, Andrew & Knott, Craig & Allen, Isaac & Huntley, Catherine & Loong, Lucy & Walburga, Yvonne & Lavelle, Katrina & Morris, Eva & Hardy, Steven & Torr, Bethany & Eccles, Diana & Turnbull, Clare & Tischkowitz, Marc & Pharoah, Paul & Antoniou, Antonis. (2023). Abstract 988: Long-term health outcomes of bilateral salpingo-oophorectomy in women with personal history of breast cancer. Cancer Research. 83. 988-988. 10.1158/1538-7445.AM2023-988.

Loong, Lucy & Huntley, Catherine & McRonald, Fiona & Santaniello, Francesco & Pethick, Joanna & Torr, Bethany & Allen, Sophie & Tulloch, Oliver & Goel, Shilpi & Shand, Brian & Rahman, Tameera & Luchtenborg, Margreet & Garrett, Alice & Barber, Richard & Bedenham, Tina & Bourn, David & Bradshaw, Kirsty & Brooks, Claire & Bruty, Jonathan & Turnbull, Clare. (2022). Germline mismatch repair (MMR) gene analyses from English NHS regional molecular genomics laboratories 1996–2020: development of a national resource of patient-level genomics laboratory records. Journal of Medical Genetics. 60. jmg-2022. 10.1136/jmg-2022-108800.

Rahman, T., Mahapatra, M., Laing, E., and Jin, Y., (2015). Evolutionary Non-linear Modelling for Selecting Vaccines Against Antigenically-variable Viruses. Bioinformatics, 31(6),834-840.

Rahman, T., Laing, E., and Jin, Y. Modelling for Predicting Antigenic Variability in Foot-and-Mouth Disease Virus. Parallel Problem Solving from Nature Workshop on Modelling Biological Systems; 2012 Sept 1; Taormina, Italy.

Rahman, T., Laing, E., and Jin, Y. Correlation Study of Amino Acid Variability and Antigenic Variability in Foot-and-Mouth Disease Virus. 9th Annual Computing Conference; 2012 March 21; Guildford, UK.


PEUGIC Root Cause Analysis

PEUGIC Root Cause Analysis

CanGene CanVar

CanGene CanVar





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