An integrative computational approach to identify disease-specific networks from PubMed literature information

A huge amount of association relationships among biological entities (e.g., diseases, drugs, and genes) are scattered in biomedicai literature. How to extract and analyze such heterogeneous data still remains a challenging task for most researchers in the biomedicai field. Natural language processing (NLP) has the potential in extracting associations among biological entities from literature. However, association information extracted through NLP can be large, noisy, and redundant which poses significant challenges to biomedicai researchers to use such information. To address this challenge, we propose a computational framework to facilitate the use of NLP results. We apply Latent Dirichlet Allocation (LDA) to discover topics based on associations. The networks extracted from each topic provide a disease-specific network for downstream bioinformatics analysis of associations for each topic. We illustrated the framework through the construction of disease-specific networks from Semantic MEDLINE, an NLP-generated association database, followed by the analysis of network properties, such as hub nodes and degree distribution. The results demonstrate that (1) LDA-based approach can group related diseases into the same disease topic; (2) the disease-specific association network follows the scale-free network property, in which hub nodes are enriched in related diseases, genes and drugs.