Our Pharmaceutical Data Mining solutions account for 3 data types: Text, Web and Numerical.
Extraction of knowledge from huge chunk of scientific literature poses a huge challenge to the informatics community. The prime suppository for biomedical literature "PUBMED" seems to grow with an extra million every 3 months. PubMed having already crossed 16 million articles, the knowledge management has become an extremely crucial factor. For a research scientist to equip with the updated and comprehensive information pertaining to his own research, it is necessary to develop systems that could easily fetch specific information in a quick time.
Since the major chunk of literary information is in the form of unstructured text, an intelligent text mining system could provide a platform for extracting and managing specific information at the entity level. For eg - Information pertaining to Genes, Proteins, Diseases, Organisms, Chemical substances.etc. It would also aid in providing insights into inter-relationships such as protein-protein, Gene-gene, Protein-Chemical, Gene-Disease and Drug-Drug interactions.
Text mining can be applied to the following areas for data curation and Database population in a semi-automated manner.
With the enormous growth of World Wide Web, the informational resource pertaining to biology and chemistry through internet have reached new heights. The heterogeneous platforms and data types spread across the internet makes it difficult to have easy access to an end user. Up-to-date there are more than 1000 web portals which provide access to various biological and Chemical databases. Accessibility to these heterogeneous databases under one roof would be a formidable solution. To achieve this it requires customized search engines and indexing mechanisms which could map the urls, web content and associated files to extract the static as well as the dynamic data.
Thus a web mining technology could be the fore runner in providing online and instant solution for gathering information across the World Wide Web.
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Automated and semi-automated systems for high throughput screening and analysis in drug research generate huge amount of numerical data The interpretation of results from these numerical data involves normalization and identification of nominal patterns which in turn requires statistical techniques to be followed. This has to be addressed through the development of component and field specific applications. Likewise the chemical structures are now efficiently handled in the form of numbers and combinations. The properties and the structural configuration of chemical compounds are now manipulated in the form of numbers. Thus a major challenge is ahead in handling these numerical data. This issue could be addressed through techniques such as Clustering, Decision Trees and Neural Networks.
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