Computational Biology

Research Domain Computational Biology

Computational Biology and Bioinformatics is an interdisciplinary research field combining fundamentals of computer science, mathematics, and statistics applied together to unravel the most complex biological processes in humans and other species. It is the most vibrant area related to disease predictions, healthcare applications and drug design. The wealth of biological data available today has brought the research field closer to the wider range of computational research such as Big Data Analytics, Data Science, Machine Learning, Image Processing, and Statistical computation.
This research domain is an interface between complex biology problems and engineering and can be applied to a broad spectrum of investigations ranging from single genes to complex biological networks. Cancer bioinformatics research has seen rapid publications of new algorithms and methods, significant biological findings, and hypothesis confirmations on different aspects of research.

Some of the applications are listed below:

• Gene identification
• Gene expression analysis
• Biological Database Searching
• Computational Systems Biology
• Computational pipelines and workflows in Bioinformatics
• NGS data analysis
• Sequence Analysis
• Genome Analysis
• Proteomics analysis
• Phylogenetic analysis
• Genome Informatics
• Text mining Applications
• Data models, representation and storage
• Biostatistics Computing for Bioinformatics
• Integrative Bioinformatics Ontology
• Text Mining
• Biological Networks Pathways in Biology
• Gene and Protein informatics
• Functional Genomics
• Translational Bioinformatics
• Bioimage informatics

The students of Computer Science and Engineering would find the field of Bioinformatics and Computational biology very interesting due to its association to data analysis and interpretation (e.g. in genomics, proteomics, and transcriptomics) with subsequent design and implementation using software packages written in R, Python or others. The use of Perl, Python and R programming languages makes the filed more suitable to Computer Scientists. Parallel computing also goes hand in hand for processing of large data and execution of complex algorithms.

Research Problem under study:

Computational Evaluation of Gene expression and Protein-protein-Interaction data for identification of common genes in Breast, Lung and Prostate cancer.

In this research, mining of gene expressions across multiple cancer types is seen to have potential to expose the genes linking to the cancerous condition. The primary objective is to observe such a set of candidate markers dominant in three cancer types. Computational and Meta-analyses on data has revealed significant results in the identification of key genes and their behavior.

Parallel Computing for Computational Biology and Bioinformatics Applications:

Broad Research Problem: Identification and characterization of cancer driver genes and mutation using parallel computing.

More and more complex bioinformatics algorithms and pipelines are being designed for interpretation of key genomic characterization of cancer using highly rich data. Deciphering genomics data for unraveling molecular functions in genetic diseases and subsequent drug design has led to the use of parallel computing tools and practices. A wide range of bioinformatics applications can be parallelized to improve performance and gain faster rates of computation. Investigators, instructors, and scholars working in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries.

Faculty Associated with the research group: