Abstract
Numerous efforts have been made for developing text-mining tools to extract information from biomedical text automatically. They have assisted in many biological tasks, such as database curation and hypothesis generation. Text-mining tools are usually different from each other in terms of programming language, system dependency and input/output format. There are few previous works that concern the integration of different text-mining tools and their results from large-scale text processing. In this paper, we describe the iTextMine system with an automated workflow to run multiple text-mining tools on large-scale text for knowledge extraction. We employ parallel processing with dockerized text-mining tools with a standardized JSON output format and implement a text alignment algorithm to solve the text discrepancy for result integration. iTextMine presently integrates four relation extraction tools, which have been used to process all the Medline abstracts and PMC open access full-length articles. The website allows users to browse the text evidence and view integrated results for knowledge discovery through a network view. We demonstrate the utilities of iTextMine with two use cases involving the gene PTEN and breast cancer and the gene SATB1.