Bibliome Mining Platform and Application for Building Metabolic Interaction Network

Abstract

Bibliome mining is a powerful technique for large-scale information extraction from textual data and connecting between biological entities as well as functional hypotheses. Currently, most bibliome mining is used for some specific studies involving genes and proteins; however, much less efforts have been focused on metabolites. In addition to application works, the focus has been on proving the concepts and algorithms, but very few reports on development of applicable text mining platform. In this study, we aimed to develop a bibliome mining platform that could be used to perform basic text mining tasks and further be used for building metabolic interaction networks. We developed a platform with the evaluated tools and subsequently tested its functions for extraction of interactions between biomolecules (e.g. genes, enzymes, proteins and metabolites) in yeast Saccharomyces cerevisiae. The results were then manually curated afterwards using KEGG LIGAND and public yeast database. Of the collected 11 text mining tools, we selected 3 suitable tools, namely ABNER, OpenNLP and LingPipe for further implementing the bibliome mining platform. In summary, a prototype of a bibliome mining platform was successfully developed and may further be used for building metabolic interaction network of S. cerevisiae. This study can be used as a basic framework for further improvement as well as extension of relevant text mining tasks and broad applications.