A dictionary to identify small molecules and drugs in free text

Hettne, Kristina; Stierum, Rob; Schuemie, Martijn J.; Hendriksen, Peter J.M.; Schijvenaars, Bob J. A.; Mulligen, Erik M. van; Kleinjans, Jos; Kors, Jan A.

doi:10.1093/bioinformatics/btp535

Cited by 129 publications

(132 citation statements)

References 44 publications

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“…These observations are worse than that reported by Oscar3 [Corbett and Murray-Rust 2006]. Hettne, et al, [Hettne et al 2009] of 45%, recall of 82% and an F-measure of 58%. These numbers are similar to our observations.…”

Section: Chemical Entity Taggingcontrasting

confidence: 63%

Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents

Sun

Mitra

Giles

et al. 2011

ACM Trans. Inf. Syst.

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End users use chemical search engines to search for chemical formulae and chemical names. Chemical search engines identify and index chemical formulae and chemical names appearing in text documents to support efficient search and retrieval in the future. Identifying chemical formulae and chemical names in text automatically has been a hard problem that has met with varying degrees of success in the past. We propose algorithms for chemical formula and chemical name tagging using Conditional Random Fields (CRFs) and Support Vector Machines (SVMs) that achieve higher accuracy than existing (published) methods. After chemical entities have been identified in text documents, they must be indexed. In order to support user-provided search queries that require a partial match between the chemical name segment used as a keyword or a partial chemical formula, all possible (or a significant number of) sub-formulae of formulae that appear in any document and all possible sub-terms (e.g., 'methyl') of chemical names (e.g., 'methylethyl ketone') must be indexed. Indexing all possible sub-formulae and sub-terms results in an exponential increase in the storage and memory requirements, and the time taken to process the indices. We propose techniques to prune the indices significantly without reducing the quality of the returned results significantly. Finally, we propose multiple query semantics to allow users to pose different types of partial search queries for chemical entities. We demonstrate empirically that our search engines improve the relevance of the returned results for search queries involving chemical entities.

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Section: Chemical Entity Taggingcontrasting

confidence: 63%

Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents

Sun

Mitra

Giles

et al. 2011

ACM Trans. Inf. Syst.

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“…They used predictions of their improved version of the ChemSpot tool 2 (Rocktäschel et al, 2012) and features derived from (i) Jochem 3 , a dictionary for the identification of small molecules and drugs in text (Hettne et al, 2009), (ii) the PHARE ontology (Coulet et al, 2011) and (iii) the ChEBI ontology (de Matos et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug–drug interaction extraction and classification

Abacha

Chowdhury

Karanasiou

et al. 2015

Journal of Biomedical Informatics

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Pharmacovigilance (PV) is defined by the World Health Organization as the science and activities related to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem. An essential aspect in PV is to acquire knowledge about Drug-Drug Interactions (DDIs). The shared tasks on DDI-Extraction organized in 2011 and 2013 have pointed out the importance of this issue and provided benchmarks for: Drug Name Recognition, DDI extraction and DDI classification. In this paper, we present our text mining systems for these tasks and evaluate their results on the DDI-Extraction benchmarks. Our systems rely on machine learning techniques using both feature-based and kernel-based methods. The obtained results for drug name recognition are encouraging. For DDI-Extraction, our hybrid system combining a feature-based method and a kernel-based method was ranked second in the DDI-Extraction-2011 challenge, and our two-step system for DDI detection and classification was ranked first in the DDI-Extraction-2013 task at SemEval. We discuss our methods and results and give pointers to future work.

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“…We have exploited available terminological resources 2 such as UMLS [4], JoChem [5], PIR Biothesaurus [6], PIR iProClass [7] or InterPro [8] (see Figure 1) to provide correspondences between the given entity identifiers. Note that, the impact of UMLS has been important in the normalization of DISO and SPE entity identifiers, whilst PIR, InterPro and JoChem in the normalization of CHED and PRGE.…”

Section: Terminological Resourcesmentioning

confidence: 99%

Exploitation of Cross-References between Terminological Resources within the CALBC Context

Jiménez-Ruiz

Rebholz‐Schuhmann

Lewin

2011

2011 22nd International Workshop on Database and Expert Systems Applications

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In this paper we have analyzed current terminological resources in order to extract available cross-references (i.e. mappings) between biomedical terminologies. These crossreferences have been applied within the CALBC challenge, an initiative which aims at automatically creating a large-scale and community-wide text corpus annotated with biomedical entities. We also show that cross-references are essential for a precise evaluation of the identifiers associated with the annotated entities by the CALBC systems.

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A dictionary to identify small molecules and drugs in free text

Abstract: The combined dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web site http://www.biosemantics.org/chemlist.

Cited by 129 publications

References 44 publications

Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents

Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents

Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug–drug interaction extraction and classification

Exploitation of Cross-References between Terminological Resources within the CALBC Context

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