| dc.description.abstract |
Biomedical research is drowning in data, yet starving for knowledge. As the volume of scientific literature is growing unprecedentedly, revolutionary measures are needed for data management. Accessibility, analysis and mining knowledge from this textual data has become a very important task. One such source is NCBI that houses a series of databases (PubMed) relevant to biotechnology and bio-medicine. It is an important resource for bioinformatics tools and services. In this paper, a system is proposed that encases all the biomedical articles of PubMed as needed by bioinformaticians. Using machine learning and natural language processing, the tool aims at assisting clinicians and biomedical researchers to understand and graphically represent the relevance of gene in a given disease context. It will also support entity-specific bio-curation searches to get a list of most effective drugs for a particular disease. The system is evaluated by using standard information retrieval measures namely, Precision, Recall and F-score to measure the relevance of search results. |
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