Integrative Computational Biology

Computational Biology, sometimes referred to as bioinformatics, is the science of using biological data to develop algorithms and relations among various biological systems. It involves the development of Tools for integrative meta-analysis, 3c-based data integration and application of Networks and OMICS data, mathematical modeling and computational simulation techniques to the study of Integrative eqtl-based analyses, High performance genomics data visualization and Laboratory information management system. The field is broadly defined and includes foundations in computer science, applied mathematics, animation, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, ecology, evolution, anatomy, neuroscience, and visualization. Computational biology is different from biological computation, which is a subfield of computer science and computer engineering using bioengineering and biology to build computers, but is similar to bioinformatics, which is an interdisciplinary science using computers to store and process biological data with applications of Gene regulatory networks in human pathogens, Drug-target disease networks with Computational approaches to drug discovery and Integrative modeling of bio molecular complexes. Computational Biology research has grown after the increased research in Genomics with major universities like Iowa State University, University Of California, and The George Washington University Concentrating on the growing topic. The Bisti Consortium has even launched the NIH and Government Programs and Initiatives in Biomedical Informatics and Computational Biology (BICB) with a list of programs concentrating on Computational Biology Research
  • Gene regulatory networks in human pathogens
  • Laboratory information management system
  • Networks and OMICS data
  • Drug-target disease networks
  • High performance genomics data visualization
  • Integrative eqtl-based analyses
  • Integrative computational oncology
  • Tools for integrative meta-analysis
  • Bayesian network inference algorithm
  • 3c-based data integration
  • Computational approaches to drug discovery
  • Integrative modeling of bio molecular complexes
  • Cancer computational biology
  • Computational biomodeling

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