CrossRef Text and Data Mining
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Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data
Arnaud Ferré, Mouhamadou Ba, Robert Bossy
Genomics Inform. 2019;17(2):e20  Published online June 27, 2019
DOI: https://doi.org/10.5808/GI.2019.17.2.e20

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Improving methods for normalizing biomedical text entities with concepts from an ontology with (almost) no training data at BLAH5 the CONTES
Genomics & Informatics. 2019;17(2):e20   Crossref logo
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An innovative hybrid approach for extracting named entities from unstructured text data
Computational Intelligence. 2019;   Crossref logo
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Comparing breast cancer treatments using automatically detected surrogate and clinically relevant outcomes entities from text
Journal of Biomedical Informatics: X. 2019;1:100005   Crossref logo
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Ontology Learning and Population from Text
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Balanced undersampling: a novel sentence-based undersampling method to improve recognition of named entities in chemical and biomedical text
Applied Intelligence. 2017;48(8):1965-1978   Crossref logo
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Leveraging MapReduce to efficiently extract associations between biomedical concepts from large text data
Microprocessors and Microsystems. 2016;46:202-210   Crossref logo
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Ontology-based clinical information extraction from physician’s free-text notes
Journal of Biomedical Informatics. 2019;98:103276   Crossref logo
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Mining strong relevance between heterogeneous entities from unstructured biomedical data
Data Mining and Knowledge Discovery. 2015;29(4):976-998   Crossref logo
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Identifying interactions between chemical entities in biomedical text
Journal of Integrative Bioinformatics. 2014;11(3):1-16   Crossref logo
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Ontology of core data mining entities
Data Mining and Knowledge Discovery. 2014;28(5-6):1222-1265   Crossref logo
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