Biological Text Mining
Description:
Technological advancements in the life sciences have enabled biologists to generate unprecedented quantities of data about biological entities. This has lead to the development of a large number of algorithms for processing and analysis of biological data. Challenges, however, remain; for instance, genes that function cooperatively need not have similar expression patterns. This suggests the use of non-numerical sources of information to explore the underlying biology. The vast body of biological literature is one such source, and its size (the MEDLINE repository alone stands at over 15 million references) necessitates the utilization of text analytics for efficient and relevant information extraction. Our research applies text mining techniques to the problem of information retrieval in the biological domain. In particular, we explore the use of text-based methods to determine similarities between biologically-significant entities such as genes and proteins.
Publications- N. Moon, B. D. Bhattarai and R. Singh, "An Experiential Approach to Interacting with Biological Information", International Symposium on Visual Computing (ISVC), 2006, LNCS 4292, pp. 1586 – 1595.
- N. Moon and R. Singh, "Experiments in Text-Based Mining and Analysis of Biological Information from MEDLINE on Functionally-Related Genes", Proc. 18th International Conference on Systems Engineering, Special Session on Computer Infrastructure for Systems Biology, 2005, IEEE Press. SSCISB2005.pdf
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