Biomedical Document Enhancement through Probabilistic Graph Clustering: Indexing and Key Phrase Mining
Keywords:
biomedical documents, gene data, feature extraction, document mappingAbstract
As the size of biomedical databases is increasing along with genes and diseases, finding key feature sets is complex due to large data sizes and sparsity problems. The process of extracting relevant biomedical features from documents, ranking them based on their probability, and clustering them is a crucial aspect of biomedical document feature extraction, ranking, and classification. To extract gene/protein features from pre-processed documents, the Abner tagger is used, and the highest probability biomedical features are identified for the graph initialization process. The graph-based clustering is performed based on the relationship between gene/protein terms in the ranked document set. To enhance the quality of the cluster, a novel graph similarity measure is employed, which maximizes the probabilistic entropy measure and prioritizes gene-based ranked document clustering. Experimental results prove that the proposed model has better improvement over the conventional models.
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