Chia-Jung Yang1,2, Jung-Hsien Chiang1. 1. Department of Computer Science and Information Engineering, National Cheng Kung University, 1, University Road, Tainan City, Taiwan. 2. Department of Radiology, Taitung Mackay Memorial Hospital, 1, Lane 303, Changsha Street, Taitung City, Taiwan.
Abstract
OBJECTIVE: A major challenge in precision medicine is the development of patient-specific genetic biomarkers or drug targets. The firsthand information of the genes associated with the pathologic pathways of interest is buried in the ocean of biomedical literature. Gene ontology concept recognition (GOCR) is a biomedical natural language processing task used to extract and normalize the mentions of gene ontology (GO), the controlled vocabulary for gene functions across many species, from biomedical text. The previous GOCR systems, using either rule-based or machine-learning methods, treated GO concepts as separate terms and did not have an efficient way of sharing the common synonyms among the concepts. MATERIALS AND METHODS: We used the CRAFT corpus in this study. Targeting the compositional structure of the GO, we introduced named concept, the basic conceptual unit which has a conserved name and is used in other complex concepts. Using the named concepts, we separated the GOCR task into dictionary-matching and machine-learning steps. By harvesting the surface names used in the training data, we wildly boosted the synonyms of GO concepts via the connection of the named concepts and then enhanced the capability to recognize more GO concepts in the text. The source code is available athttps://github.com/jeroyang/ncgocr. RESULTS: Named concept gene ontology concept recognizer (NCGOCR) achieved 0.804 precision and 0.715 recall by correct recognition of the non-standard mentions of the GO concepts. DISCUSSION: The lack of consensus on GO naming causes diversity in the GO mentions in biomedical manuscripts. The high performance is owed to the stability of the composing GO concepts and the lack of variance in the spelling of named concepts. CONCLUSION: NCGOCR reduced the arduous work of GO annotation and amended the process of searching for the biomarkers or drug targets, leading to improved biomarker development and greater success in precision medicine.
OBJECTIVE: A major challenge in precision medicine is the development of patient-specific genetic biomarkers or drug targets. The firsthand information of the genes associated with the pathologic pathways of interest is buried in the ocean of biomedical literature. Gene ontology concept recognition (GOCR) is a biomedical natural language processing task used to extract and normalize the mentions of gene ontology (GO), the controlled vocabulary for gene functions across many species, from biomedical text. The previous GOCR systems, using either rule-based or machine-learning methods, treated GO concepts as separate terms and did not have an efficient way of sharing the common synonyms among the concepts. MATERIALS AND METHODS: We used the CRAFT corpus in this study. Targeting the compositional structure of the GO, we introduced named concept, the basic conceptual unit which has a conserved name and is used in other complex concepts. Using the named concepts, we separated the GOCR task into dictionary-matching and machine-learning steps. By harvesting the surface names used in the training data, we wildly boosted the synonyms of GO concepts via the connection of the named concepts and then enhanced the capability to recognize more GO concepts in the text. The source code is available athttps://github.com/jeroyang/ncgocr. RESULTS: Named concept gene ontology concept recognizer (NCGOCR) achieved 0.804 precision and 0.715 recall by correct recognition of the non-standard mentions of the GO concepts. DISCUSSION: The lack of consensus on GO naming causes diversity in the GO mentions in biomedical manuscripts. The high performance is owed to the stability of the composing GO concepts and the lack of variance in the spelling of named concepts. CONCLUSION: NCGOCR reduced the arduous work of GO annotation and amended the process of searching for the biomarkers or drug targets, leading to improved biomarker development and greater success in precision medicine.
Authors: Christopher Funk; William Baumgartner; Benjamin Garcia; Christophe Roeder; Michael Bada; K Bretonnel Cohen; Lawrence E Hunter; Karin Verspoor Journal: BMC Bioinformatics Date: 2014-02-26 Impact factor: 3.169
Authors: Amaia Sangrador-Vegas; Alex L Mitchell; Hsin-Yu Chang; Siew-Yit Yong; Robert D Finn Journal: Database (Oxford) Date: 2016-03-19 Impact factor: 3.451