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Oisin Brady

Oisin Brady

Research student

School of Computer Science and Informatics

Abacws, Room Room 3.69, Senghennydd Road, Cathays, Cardiff, CF24 4AG


I am a computer science PhD student with a strong interest in overlaps between computational and biological domains of research. I have previous experience with "omic" data analysis using statistical and machine learning techniques where I was involved in annotation for unmapped regions of prokaryotic genomes. Currently, I have a strong interest in precision medicine, which often manipulates large, longitudinal, and multi-sourced datasets in order to provide highly accurate and patient specific diagnoses, prognoses and treatment pathways.



Cancer Patient Digital Twins to Investigate Disease Fragmentation and its Impact on Drug Response in Acute Myeloid Leukaemia (AML) trials.

My research focuses on implementing cancer patients' digital twins to investigate disease fragmentation and its impact on drug/therapy response in Acute Myeloid Leukaemia (AML) trials. This involves the analysis of aggregate AML patient data using machine learning techniques and the implementation of a digital twin system based of time-series based patient information to predict the most effective individual-based treatment strategies for future patients. I'm working as part of the Interdisciplinary Precision Oncology Cardiff Hub (IPOCH,

Funding sources

Engineering and Physical Sciences Research Council RTSG, Tuition and Stipend.


Computer Science 1st class (BSc) postgraduate from Aberystwyth University. Experience with Python, Java, C, C++, HTML, CSS, SQL(PostgreSQL), Javascript, web-development frameworks such as Django, and CI/CD tools like Jenkins. Experience using and manipulating FASTA data for prokaryotic genome sequence analysis with self written scripts aswell as tools such as BLAST. Experience as an R&D developer and systems operator in a corporate environment.

Honours and awards

1st class honors in Computer Science with an Integrated Year in Industry (Aberystwyth University)


Research themes


  • precision oncology
  • Genomics