The Computational Biology Lab (CBL) is a research group in the Department of Computer and Data Sciences at Case Western Reserve University. Our research group consists of undergraduate and graduate students and the team is led by Professor Jing Li . Members of our team majorly comes from Computer Science background and have a passion to find answers to the problems in Bioinformatics subject area. We are partnered in collaborative researches with several medical centers such as Cleveland Clinic and University Hospitals and reseach organizations.

Our research interests are in the areas of bioinformatics and computational biology, statistical genomics, personal genomics and functional genomics, with a particular emphasis on the design and application of efficient combinatorial and statistical algorithms to challenging biological problems, and on the development of user-friendly software tools. More specifically, we have been mainly working on problems arising from analysis of human genomic variations, including haplotype inference from family data and family-based association mapping, disease association mapping based on haplotype similarities, SNP (single nucleotide polymorphism) subset selection within multi-stage designs, copy number variation and structure variation detection, investigation of epistatic effects based association studies, rare SNP detection and calling using pooled samples, disease gene ranking, and management and visualization of genome-wide association results. Together with colleagues and students, we have also investigated computational approaches for gene co-expression data analysis, identification of gene expression patterns across cancer stages, computational analysis of non-coding RNAs, evolutionary analysis of biological networks, as well as drug target prediction and drug repositioning. Significant progress has been made in many of these areas, including publications in prominent journals, leading international conferences, book chapters, numerous invited talks and conference/workshop presentations, as well as software tools.

We use computational algorithms, tools and techniques to process and analyze data. Then we design algorithms to solve the problems in the field using some modern computational techniques like statistical learning, machine learning and deep learning. This way our mission is to address the gaps between Computer Sciecne and Biology. Our research mainly focus on the areas of bioinformatics and computational biology, statistical genomics, personal genomics and functional genomics, with a particular emphasis on the design and application of efficient combinatorial and statistical algorithms to challenging biological problems, and on the development of user-friendly software tools. One major area of research is development of computational solutions for the analysis of human genomic variations, which includes:

• ༓ Haplotype inference based on family data
• ༓ Disease association mapping
• ༓ Structure variation and copy number variation detection
• ༓ Rare variants identification using pooled samples

Another area of research is in integration of omics data, which includes:

• ༓ Disease gene ranking using multiple data sources
• ༓ Integration of methylation and expression
To find more information about our work please visit our research work sections.