Mining MEDLINE for implicit links between dietary substances and diseases.

MOTIVATION: Text mining systems aim at knowledge discovery from text collections. This work presents our text mining algorithm and demonstrates its use to uncover information that could form the basis of new hypotheses. In particular, we use it to discover novel uses for Curcuma longa, a dietary substance, which is highly regarded for its therapeutic properties in Asia. RESULTS: Several disease were identified that offer novel research contexts for curcumin. We analyze select suggestions, such as retinal diseases, Crohn's disease and disorders related to the spinal cord. Our analysis suggests that there is strong evidence in favor of a beneficial role for curcumin in these diseases. The evidence is based on curcumin's influence on several genes, such as COX-2, TNF-alpha, JNK, p38 MAPK and TGF-beta. This research suggests that our discovery algorithm may be used to suggest novel uses for dietary and pharmacological substances. More generally, our text mining algorithm may be used to uncover information that potentially sheds new light on a given topic of interest. AVAILABILITY: Contact authors.

Semantic relations asserting the etiology of genetic diseases.

Considerable research is being directed at extracting molecular biology information from text. Particularly challenging in this regard is to identify relations between entities, such as protein-protein interactions or molecular pathways. In this paper we present a natural language processing method for extracting causal relations between genetic phenomena and diseases. After presenting the results of preliminary evaluation, we suggest the use of a graphical display application for viewing the semantic predications produced by the system.

NLP-based information extraction for managing the molecular biology literature.

We present research aimed at devising a tool for using natural language processing to identify and extract biomedical information from text for the purpose of assisting researchers in molecular biology manage large amounts of information. A pilot project based on the molecular genetics of diabetes demonstrates our ability to explore the interaction of genomic phenomena and clinical findings. We suggest the cooperation of this extracted information with systems for clustering text and constructing labeled networks of data.