Text mining involves recognizing useful patterns from a wealth of information hidden latent in unstructured text and deducing explicit relationships among data entities by using data mining tools. Harvesting chemical data from textual information is a challenging task. Text mining of Biomedical literature is essential for building biological network connecting genes, proteins, drugs, therapeutic categories, side effects etc. related to diseases of interest. We present an approach for chemically significant textmining biomedical literature mostly in terms of not so obvious hidden relationships and build biological network applied for the textmining of scientific literature related to human diseases like Tuberculosis and Malaria. The methods, tools and data used for building biological networks using a distributed computing environment previously used for ChemXtreme and ChemStar applications will be discussed. The architecture of open source based tool CheminfoCloud developed for this purpose will be presented.

ChemInfoCloud: Opensource based Cloud compatible chemical textmining tools for harvesting largescale medical literature

248th National Meeting (2014)

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