The Bioinformatics and Computational Biology (BCB) research group in UCSC conducts interdisciplinary research at the interface between Computer and Biological Sciences. Members of the group are from various sub domains including machine learning, bioinformatics, network science, high performance computing and statistics,

Our research primarily involves conducting quantitative studies, such as pattern recognition and network analysis, to discover biological knowledge; and developing computational techniques, algorithms, tools and statistical models to analyze various biological data and make predictions.

Some of the specific areas our projects are focused on include:

  • Genomics: gene regulatory network reconstruction, primer detection
  • Proteomics: protein-protein interaction prediction, protein complex detection
  • Evolutionary biology: gene transfer prediction, identification of evolutionary patterns
  • Cancer genomics
  • Immunoinformatics



  1. Identifying subpopulations in tumor samples

Cancer is major life threatening disease throughout the world. Current cancer research revealed that many solid cancers show wide genetic heterogeneity between as well as within individual tumors. Therefore tumor sub population analysis is an emerging area in cancer research. However, statistical methods used for analyzing tumors usually based on “all-or- none decision”. This may be misleading for biopsy sample which contains a mixture of subtypes and normal cell contamination. Developing a method to differentiate the sub populations in cancer biopsy by eliminating normal cell contamination by genomic data analysis will be a promising area in personalized medicine.


  1. Studying coevolution of Dengue virus and Host

The number of cases and severity of disease associated with Dengue infection in Sri Lanka has been increasing since 1989. Studies shows that prior to 2009 DENV2 and DENV3 serotypes are dominant and DENV3 caused the epidemics in the years of 2002 and 2004. In 2009 DENV1 was the major disease causing serotype in Sri Lanka and resulted in 346 deaths. Epidemic in 2012 also caused by DENV1.

Why coevolution is critical?

Evolution of one species in response to the change in another species or change in environment. In Coevolution of virus and immune system, if humans develop resistance to a virus, the virus could evolve to become resistant to the newly evolved human resistance.



Dr. Ruvan Weerasinghe, University of Colombo School of Computing, SL

Ms. Rupika Wijesinghe, University of Colombo School of Computing, SL

Dr. Mindika Premachandra, University of Colombo School of Computing, SL

Prof. Mahesan Niranjan, University of Southampton, UK

Dr. Vinod N. Rajapakshe, National Institute of Health, USA

Dr. Yawwani Poornima, University of Southampton, UK

Luke Nitish, University of Alberta, Canada

Warunika Ranaweera, Simon Fraser University, canada

Shazan Jabbar, University of Alberta, Canada

Bhagya Senadeera,  Mphil student,  University of Colombo School of Computing

Karthika Mayan, Research Assistant


Grants :

University Grants Commission



Ms. Rupika Wijesinghe, Senior Lecturer, University of Colombo School of Computing.


Phone: +94 011 2158976, +94-0777872641


• V. H. W Dissanayake, S. M. Widanagamaachchi, A. R. Weerasinghe and C. R. Wijesinghe, “Analyzing the human specificity of pre-eclampsia,” in proceedings of the Fifth International Benelux Bioinformatics Conference, Liege, Belgium: Dec, 2009, pp. 115.
• M. S. H. Perera, A. R. Weerasinghe and C. R. Wijesinghe, “Analyzing conserved non-coding DNA sequences in the human genome,” in proceedings of the Asian Regional Conference on Systems Biology, Kuala Lumpur, Malaysia, Nov, 2010.
• M. M. A. T. N. Mannapperuma, C. R. Wijesinghe and A. R. Weerasinghe, “Learning the diversity and evolutionary pattern of the dengue virus,” in proceedings of the International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2012), Singapore, Mar, 2012, pp. 6-11.
• P. G. Sudasinghe, C. R. Wijesinghe and A. R. Weerasinghe, “Prediction of Horizontal Gene Transfer in Escherichia coli using Machine Learning,” in proceedings of the International Conference on Advances in ICT for Emerging Regions. (in press)