Text Mining Interpreting Knowledge Discovery from Biomed Articles

Abstract

The biggest challenge for text and data mining in biomedical informatics is to impact the discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions for understanding the knowledge basis from biomed articles or documents. However, formulation of a flexible and general approach for integrating heterogeneous data and knowledge sources for discovery is elusive and highly dependent upon the specific underlying scientific question. This target has been taken as our work to interpret the knowledge discovery from Biomed articles. Our work has been framed with base of Keyword search, Information Retrieval and Information Extraction to bound the knowledge of articles in databases like Pub Med, Oxford journals etc., Thus, the true impact of text and data mining is only realized if it goes beyond the methods for extraction and indexing into enabling understanding of the entities like Protein-Protein Interaction, Gene and Human diseases relationship presented in articles, documents. It acts as an underpinnings process to form a network is modelled here.