A text mining approach for identifying herb-chemical relationships from biomedical articles

DTMBIO 2015 - Proceedings of the ACM 9th International Workshop on Data and Text Mining in Biomedical Informatics, co-located with CIKM 2015

Volumn , Issue , 2015, Pages 25-

  Choi, Wonjun ^a   Lee, Hyunju ^a  

^a Gwangju Institute of Science and Technology (GIST)   (South Korea)

Author keywords

Biomedical articles; Data mining; Herb; Herb chemical corpus; Herb chemical relationship; Natural product; Text mining; Traditional Chinese Medicine

Indexed keywords

ABSTRACTING; BIOINFORMATICS; BIOMEDICAL ENGINEERING; COMPUTATIONAL LINGUISTICS; DATABASE SYSTEMS; DRUG INTERACTIONS; INDICATORS (CHEMICAL); INFORMATION SCIENCE; MEDICINE; NATURAL LANGUAGE PROCESSING SYSTEMS; SHIPS; TEXT PROCESSING;

BIOMEDICAL ARTICLES; CHEMICAL RELATIONSHIPS; HERB; HERB-CHEMICAL CORPUS; NATURAL PRODUCTS; TEXT MINING; TRADITIONAL CHINESE MEDICINE;

DATA MINING;

EID: 84960910112     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2811163.2811178     Document Type: Conference Paper

References (4)

1. B. Baldwin. and B. Carpenter. Lingpipe. http://www.aliasi.com/lingpipe, 2000.
2. S. Federhen. The ncbi taxonomy database. Nucleic Acids Res, 40(D1):D136-D143, December 2011.
3. T. Rocktaschel., M. Weidlich., and U. Leser. Chemspot: a hybrid system for a chemical named entity recognition. Bioinformatics, 28(12):1633-1640, April 2012.
4. R. Xue., Z. Fang., and M. Z. et al. Tcmid: traditional chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res, 41(D1):D1089-D1095, November 2012.