Dataset Information

CheNER: chemical named entity recognizer.

ABSTRACT: MOTIVATION:Chemical named entity recognition is used to automatically identify mentions to chemical compounds in text and is the basis for more elaborate information extraction. However, only a small number of applications are freely available to identify such mentions. Particularly challenging and useful is the identification of International Union of Pure and Applied Chemistry (IUPAC) chemical compounds, which due to the complex morphology of IUPAC names requires more advanced techniques than that of brand names. RESULTS:We present CheNER, a tool for automated identification of systematic IUPAC chemical mentions. We evaluated different systems using an established literature corpus to show that CheNER has a superior performance in identifying IUPAC names specifically, and that it makes better use of computational resources. AVAILABILITY AND IMPLEMENTATION:http://metres.udl.cat/index.php/9-download/4-chener, http://chener.bioinfo.cnio.es/

SUBMITTER: Usie A

PROVIDER: S-EPMC3967102 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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CheNER: chemical named entity recognizer.

Usié Anabel A Alves Rui R Solsona Francesc F Vázquez Miguel M Valencia Alfonso A

Bioinformatics (Oxford, England) 20131113 7

<h4>Motivation</h4>Chemical named entity recognition is used to automatically identify mentions to chemical compounds in text and is the basis for more elaborate information extraction. However, only a small number of applications are freely available to identify such mentions. Particularly challenging and useful is the identification of International Union of Pure and Applied Chemistry (IUPAC) chemical compounds, which due to the complex morphology of IUPAC names requires more advanced techniqu ...[more]

PMID: 24227678

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Project description:MotivationApplication of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical-chemical properties and biological activities. Scientific texts represent low-formalized sources of information. Most methods aimed at CNER are based on machine learning approaches, including conditional random fields and deep neural networks. In general, most machine learning approaches require either vector or sparse word representation of texts. Chemical named entities (CNEs) constitute only a small fraction of the whole text, and the datasets used for training are highly imbalanced.Methods and resultsWe propose a new method for extracting CNEs from texts based on the naïve Bayes classifier combined with specially developed filters. In contrast to the earlier developed CNER methods, our approach uses the representation of the data as a set of fragments of text (FoTs) with the subsequent preparati`on of a set of multi-n-grams (sequences from one to n symbols) for each FoT. Our approach may provide the recognition of novel CNEs. For CHEMDNER corpus, the values of the sensitivity (recall) was 0.95, precision was 0.74, specificity was 0.88, and balanced accuracy was 0.92 based on five-fold cross validation. We applied the developed algorithm to the extracted CNEs of potential Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. A set of CNEs corresponding to the chemical substances evaluated in the biochemical assays used for the discovery of Mpro inhibitors was retrieved. Manual analysis of the appropriate texts showed that CNEs of potential SARS-CoV-2 Mpro inhibitors were successfully identified by our method.ConclusionThe obtained results show that the proposed method can be used for filtering out words that are not related to CNEs; therefore, it can be successfully applied to the extraction of CNEs for the purposes of cheminformatics and medicinal chemistry.

Dataset Information

CheNER: chemical named entity recognizer.

Publications

CheNER: chemical named entity recognizer.

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