基于语义的疾病相关蛋白质知识抽取

doi:10.6040/j.issn.1671-9352.1.2015.025

Abstract

Abstract: With the rapid development of genomics and high-throughput technologies, large amount of biomedical literatures about genes and proteins appear. Meanwhile, the use of text mining technology discovery and excavation of new, valuable knowledge of protein from the mass of medical texts has become possible. This paper presents a system which extracts the relations between proteins and certain diseases from biomedical literature based on semantic output generated by SemRep, and then extracts novel, valuable protein knowledge. The system summarizes the salient relations by the salient extraction algorithm using the specific subject MEDLINE corpus. Subsequently, the results extracted by the system are compared with data from KEGG database. Implementation of the system has important significance for understanding the major causes of many diseases, protein function prediction and drug-aided design.

Key words: KEGG, semantic relation, SemRep, information extraction

CLC Number:

TP391

LI Zhi-heng, YANG Zhi-hao, LIN Hong-fei. Semantic output output-based disease-protein knowledge extraction[J].JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(3): 104-110.

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