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Breast cancer and osteoporosis are two most common diseases in postmenopausal women. Both diseases are multi-factorial and involve complex interactions of many genes. Since it is very difficult to review all published papers manually to understand interaction between genes pertaining to these two diseases, we employed text mining system which is an automated approach to search for these gene interactions...
Automated extraction of knowledge from voluminous documents is a vast research area. Text mining is a promising approach for extracting knowledge from unstructured textual documents. The objective of this paper is to mine documents pertaining to Ayurveda, which are retrieved from PubMed into a databank, and find novel transitive associations among biological objects. This paper discusses the extraction...
Extraction of knowledge from biomedical literature is one of the major problems for researchers. This primarily involves identification of novel associations between biological objects (genes, proteins, diseases, medicines etc.). These associations are commonly extracted by mining biomedical resources such as the PUBMED which contains a large volume of information. An automated approach towards this...
There has been a considerable amount of recent research in extraction of various kinds of binary associations (e.g., gene-gene, gene-protein, protein-protein, etc) using different text mining approaches. However, an important aspect of such associations is identifying the context in which such associations occur (e.g., "gene A activates protein B in the context of disease C in organ D under the...
Text mining methods are used in this paper to extract associations among biological objects. Transitive association methods using metadata (MeSH terms) have the potential to discover implicit associations without relying on explicit co-occurrence of objects of interest. To avoid costly manual metadata assignment and deal with missing metadata, automated metadata generation methods are described in...
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