Relation information extraction using a comprehensive representation scheme: applications to oncology

Abstract

Information extraction (IE) is a task of identifying relevant information from input text and producing structured data as output. While explicit expressions describing the target information are the basis for the development of IE systems, in-depth analysis of the input text becomes necessary when the information is conveyed implicitly in the text. In this dissertation, we address a specialized IE method for gene-cancer relations conveyed implicitly in biomedical text. Automatic identification of gene-cancer relations from a large volume of biomedical text is an important task for cancer research, since changes in genes are known to be the main cause of oncogenesis. In particular, it is essential to understand how a gene affects a cancer and to classify genes into oncogenes (genes that cause cancers), tumor suppressor genes (genes that protect cells from cancers) and biomarkers (genes that indicate normal or cancerous states), since such classification facilitates the process of treatment and diagnosis method development. However, despite the high volume of information on such gene classes that is conveyed implicitly with detailed descriptions about gene and cancer properties, there is not yet an IE system that is targeted at such implicit information. In this dissertation, we claim that in order to classify genes into candidates of oncogenes, tumor suppressor genes and biomarkers, gene-cancer relations described in biomedical text must be characterized with 1) how a gene changes; 2) how a cancer changes; and 3) the causality between the gene and the cancer. We propose a comprehensive representation scheme that identifies gene-cancer relations upon the three aspects above and use it for developing an advanced text mining system for oncogenes, tumor suppressor genes and biomarkers. The proposed representation scheme is shown to be adequate enough to describe the set of information that can be identified objectively from biomedical text, giving rise to an ann...