Mining Biomedical Literature to Explore Interactions between Cancer Drugs and Dietary Supplements.

Interactions between cancer drugs and dietary supplements are clinically important and have not been extensively investigated through mining of the biomedical literature. We report on a previously introduced method now enhanced by machine learning-based filtering. Potential interactions are extracted by using relationships in the form of semantic predications. Semantic predications stored in SemMedDB, a database of structured knowledge generated from MEDLINE, were filtered and connected by two interaction pathways to explore potential drug-supplement interactions (DSIs). The lasso regression filter was trained by using SemRep output features in an expert annotated corpus and used to rank retrieved predications by predicted precision. We found not only known interactions but also inferred several unknown potential DSIs by appropriate filtering and linking of semantic predications.

Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs.

In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. We used the semantic relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations using SemRep, to extract potential relationships using knowledge of cancer drugs pathways. Two cancer drugs pathway schemas were constructed using these relationships extracted from SemMedDB. Through both pathway schemas, we found drugs already used for prostate cancer therapy and drugs not currently listed as the prostate cancer medications. Our study demonstrates that the appropriate linking of relevant structured semantic relationships stored in SemMedDB can support the discovery of potential prostate cancer drugs.

Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.