Dr Valeriia Haberland
Lecturer in Computer Science
Summary
I have been appointed as Lecturer in Computer Science in 2023. My research interests lie in the intersection of computing and biological sciences. Currently, I'm interested in modelling and predicting cancer aggressiveness based on the available molecular profiles using the state-of-the-art machine learning techniques. My other interests also include epidemiological causal inference to find associations between genetically translatable factors and disease outcomes. Developing the web applications and underlying infrastructure to support further research in these areas are also of interest.
Qualifications
- PhD in Computer Science, King's College London (2015)
- Fellow HEA (FHEA) - awarded in Oct 2023.
Newest selected publications
Buhigas, C., Warren, AY., Leung, WK., Whitaker, HC., Luxton, HJ., Hawkins, S., et al. (2022) 'The architecture of clonal expansions in morphologically normal tissue from cancerous and non-cancerous prostates'. Molecular Cancer, 21 (1). pp. 1 - 13. ISSN: 1476-4598 Open Access Link
Liu, Y., Elsworth, B., Erola, P., Haberland, V., Hemani, G., Lyon, M., et al. (2020) 'EpiGraphDB: A database and data mining platform for health data science'. Bioinformatics, 37 (9). pp. 1304 - 1311. ISSN: 1367-4803 Open Access Link
Zheng, J., Haberland, V., Baird, D., Walker, V., Haycock, PC., Hurle, MR., et al. (2020) 'Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases'. Nature Genetics, 52 (10). pp. 1122 - 1131. ISSN: 1061-4036 Open Access Link
Lightbody, G., Haberland, V., Browne, F., Taggart, L., Zheng, H., Parkes, E. and et al. (2019) 'Review of applications of high-throughput sequencing in personalized medicine: Barriers and facilitators of future progress in research and clinical application'. Briefings in Bioinformatics, 20 (5). pp. 1795 - 1811. ISSN: 1467-5463 Open Access Link
Hatzikotoulas, K., Tachmazidou, I., Southam, L., Esparza-Gordillo, J., Haberland, V., Zheng, J., et al. (2019) 'Genome-wide analyses using UK biobank data povide new therapeutic targets for osteoarthritis'. Elsevier BV. pp. S58 - S59. ISSN: 1063-4584