Bioinformatics and computational biology are interdisciplinary in nature, encompassing computer science, engineering and mathematics with biology and medicine. These areas of study are helping professionals infer novel biological hypotheses and conclusions using advanced computational and analytical approaches.
Our department's bioinformatics and computational biology program focuses on the development and application of data analysis and mining algorithms, computational methods and infrastructures, and systems-biology approaches that integrate experimental and clinical technologies to address fundamental challenges in biomedicine.
The program’s faculty and staff are supported by a variety of intramural and extramural funding sources including, but not limited to, the following: the National Institute of Health (NIH, including NLM, NIMH and NIGMH), the National Cancer Institute (NCI), the National Science Foundation (NSF), the Department of Defense (DOD), the Department of Energy (DOE) and multiple foundations. Examples of active research and development efforts within the program include:
- The development and application of network mining algorithms integrating high-throughput gene expression data from multiple sources to identify new biomarkers for cancers (e.g., breast cancer, glioblastoma, chronic lymphocytic leukemia, colon cancer) and other diseases such as idiopathic pulmonary fibrosis;
- The design and implementation of a decentralizing labeling scheme, the k -neighborhood Decentralization Labeling Scheme or kDLS, to index the UMLS graph for answering graph-based queries and to efficiently find distance, path and a summary of paths between two concepts. This decentralizing labeling scheme supports highly efficient querying of related biomedical terms enabling high-throughput hypothesis generation and large-scale knowledge discovery.
- The development of novel algorithms for data comparison and enrichment detection in next generation sequencing (NGS) technologies including ChIP-seq, RNA-seq, and MDBC-seq.
- The application of machine learning algorithms and network and pathway analysis methods for integrative analysis of cancer epigenetics and gene expression data using multiple study epigenetic events associated with drug resistance in breast and ovarian cancer.
- The development of advanced quantitative phenotyping tools including large scale microscopic image analysis algorithms and pattern recognition methods for characterizing cell and tissue distributions in biological and clinical studies with applications in biomarker discovery for cancers and cell distribution patterns during brain development.
Interested in taking coursework related to Bioinformatics and Computational Biology?