ProMiner: rule-based protein and gene entity recognition

By Daniel Hanisch, Katrin Fundel, Heinz-Theodor Mevissen, Ralf Zimmer, Juliane Fluck. BMC Bioinformatics, 2005

345 citations recorded by the source corpus.

BackgroundIdentification of gene and protein names in biomedical text is a challenging task as the corresponding nomenclature has evolved over time. This has led to multiple synonyms for individual genes and proteins, as well as names that may be ambiguous with other gene names or with general English words. The Gene List Task of the BioCreAtIvE challenge evaluation enables comparison of systems addressing the problem of protein and gene name identification on common benchmark data.MethodsThe ProMiner system uses a pre-processed synonym dictionary to identify potential name occurrences in the biomedical text and associate protein and gene database identifiers with the detected matches. It follows a rule-based approach and its search algorithm is geared towards recognition of multi-word names [1]. To account for the large number of ambiguous synonyms in the considered organisms, the system has been extended to use specific variants of the detection procedure for highly ambiguous and case-sensitive synonyms. Based on all detected synonyms for one abstract, the most plausible database identifiers are associated with the text. Organism specificity is addressed by a simple procedure based on additionally detected organism names in an abstract.ResultsThe extended ProMiner system has been applied to the test cases of the BioCreAtIvE competition with highly encouraging results. In blind predictions, the system achieved an F-measure of approximately 0.8 for the organisms mouse and fly and about 0.9 for the organism yeast.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/37116aac68b32a4690dd6132d50cbea1bdd9efd9

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Citations in the corpus

Biomedical text mining and its applications in cancer research (2013, neutral): “[61,62] used an association-based technique and a natural language processing tool to generate a ranked list of genes associated with diseases and extracted the relations between genes and lipopolysaccharide.”
DISEASES: Text mining and data integration of disease–gene associations (2014, methodology): “For this purpose the quality of the dictionary is obviously very important; the best-performing methods for NER according to blind assessments rely on carefully curated dictionaries to eliminate synonyms that give rise to many false positives [6,7].”
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text (2011, methodology): “trained CRFs on textual features enhanced with the output of a rule-based named entity recognition system.”
Extraction of potential adverse drug events from medical case reports (2012, methodology): “For this purpose, ProMiner, a dictionary-based named entity recognition system [12] was employed.”
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval (2018, methodology): “It employs ”ProMiner” 2 (Hanisch et al., 2005) for Protein/Gene and Disease entity recognition and regular expression based text scanning using syn-onyms from ”DrugBank” 3 and ”PubChem” 4 for the identiﬁcation of Drugs and manually edited regular expressions, relating to ”HGVS” 5 standards, to…”
A Survey on Deep Learning for Named Entity Recognition (2018, background): “[43] proposed ProMiner, which leverages a pre-processed synonym dictionary to identify protein mentions and potential gene in biomedical text.”
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports (2012, methodology): “After the harmonization procedure, in order to analyze the semantic distribution of entities in the ADE corpus, the annotated names of drugs and adverse effects were mapped to standard ontologies using the ProMiner [28] system.”
Overview of BioCreative II gene normalization (2008, methodology): “Team 13 (Juliane Fluck, Fraunhofer Institute for Algorithms and Scientific Computing [SCAI]) The ProMiner system [25,26] relies on an approximate string matching algorithm in conjunction with an automatically curated dictionary.”
LINNAEUS: A species name identification system for biomedical literature (2010, background): “For example, if the species and locations of species mentions discussed in a document were known, it could provide important information to guide the recognition, normalization and disambiguation of other entities like genes [5-7], since genes are often mentioned together with their host species.”
Corpus annotation for mining biomedical events from literature (2008, background): “These lexical resources will bring further progress in NLP-based TM for the domain [22-28].”