Biomedical Literature Mining

Despite an influx of molecular data in the form of sequences, structure, transcription profiles etc., most of the protein interaction information relevant to cell biology research still exists strictly in the scientific literature which is written in a natural language that computers cannot easily manipulate. Automatically mining and extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. In this talk, we present a novel approach Bio-IEDM (Biomedical Information Extraction and Data Mining) to integrate text mining and predictive modeling to analyze biomolecular network from biomedical literature databases. Our method consists of two phases. In phase 1, we discuss a semi-supervised efficient learning approach to automatically extract biological relationships such as protein-protein interaction, protein-gene interaction from the biomedical literature databases to construct the biomolecular network. In phase 2, we present a novel clustering algorithm to analyze the biomolecular network graph to identify biologically meaningful subnetworks (communities). The clustering algorithm considers the characteristics of the scale-free network graphs and is based on the local density of the vertex and its neighborhood functions that can be used to find more meaningful clusters with different density level. The experimental results indicate our approach is very effective in extracting biological knowledge from a huge collection of biomedical literatures. The integration of data mining and information extraction provides a promising direction for analyzing the biomolecular network.