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Open Access Highly Accessed Research article

CoMAGC: a corpus with multi-faceted annotations of gene-cancer relations

Hee-Jin Lee1, Sang-Hyung Shim2, Mi-Ryoung Song2, Hyunju Lee3 and Jong C Park1*

Author Affiliations

1 Department of Computer Science, KAIST, 291 Daehak-ro, Daejeon, Republic of Korea

2 School of Life Sciences, Bioimaging Research Center and Cell Dynamics Research Center, Gwangju Institute of Science and Technology, 132 Cheomdan-gwagiro, Gwangju, Republic of Korea

3 School of Information and Communications, Gwangju Institute of Science and Technology, 132 Cheomdan-gwagiro, Gwangju, Republic of Korea

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BMC Bioinformatics 2013, 14:323  doi:10.1186/1471-2105-14-323

Published: 14 November 2013

Abstract

Background

In order to access the large amount of information in biomedical literature about genes implicated in various cancers both efficiently and accurately, the aid of text mining (TM) systems is invaluable. Current TM systems do target either gene-cancer relations or biological processes involving genes and cancers, but the former type produces information not comprehensive enough to explain how a gene affects a cancer, and the latter does not provide a concise summary of gene-cancer relations.

Results

In this paper, we present a corpus for the development of TM systems that are specifically targeting gene-cancer relations but are still able to capture complex information in biomedical sentences. We describe CoMAGC, a corpus with multi-faceted annotations of gene-cancer relations. In CoMAGC, a piece of annotation is composed of four semantically orthogonal concepts that together express 1) how a gene changes, 2) how a cancer changes and 3) the causality between the gene and the cancer. The multi-faceted annotations are shown to have high inter-annotator agreement. In addition, we show that the annotations in CoMAGC allow us to infer the prospective roles of genes in cancers and to classify the genes into three classes according to the inferred roles. We encode the mapping between multi-faceted annotations and gene classes into 10 inference rules. The inference rules produce results with high accuracy as measured against human annotations. CoMAGC consists of 821 sentences on prostate, breast and ovarian cancers. Currently, we deal with changes in gene expression levels among other types of gene changes. The corpus is available at http://biopathway.org/CoMAGC webciteunder the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 webcite).

Conclusions

The corpus will be an important resource for the development of advanced TM systems on gene-cancer relations.