19,769 research outputs found
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
A text-mining system for extracting metabolic reactions from full-text articles
Background: Increasingly biological text mining research is focusing on the extraction of complex relationships
relevant to the construction and curation of biological networks and pathways. However, one important category of
pathwayâmetabolic pathwaysâhas been largely neglected.
Here we present a relatively simple method for extracting metabolic reaction information from free text that scores
different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence
and location of stemmed keywords. This method extends an approach that has proved effective in the context of the
extraction of proteinâprotein interactions.
Results: When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our
method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the
well-known protein-protein interaction extraction task.
Conclusions: We conclude that automated metabolic pathway construction is more tractable than has often been
assumed, and that (as in the case of proteinâprotein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed
Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions
Background. Drug-drug interaction (DDI) is a major cause of morbidity and
mortality. [...] Biomedical literature mining can aid DDI research by
extracting relevant DDI signals from either the published literature or large
clinical databases. However, though drug interaction is an ideal area for
translational research, the inclusion of literature mining methodologies in DDI
workflows is still very preliminary. One area that can benefit from literature
mining is the automatic identification of a large number of potential DDIs,
whose pharmacological mechanisms and clinical significance can then be studied
via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We
implemented a set of classifiers for identifying published articles relevant to
experimental pharmacokinetic DDI evidence. These documents are important for
identifying causal mechanisms behind putative drug-drug interactions, an
important step in the extraction of large numbers of potential DDIs. We
evaluate performance of several linear classifiers on PubMed abstracts, under
different feature transformation and dimensionality reduction methods. In
addition, we investigate the performance benefits of including various
publicly-available named entity recognition features, as well as a set of
internally-developed pharmacokinetic dictionaries. Results. We found that
several classifiers performed well in distinguishing relevant and irrelevant
abstracts. We found that the combination of unigram and bigram textual features
gave better performance than unigram features alone, and also that
normalization transforms that adjusted for feature frequency and document
length improved classification. For some classifiers, such as linear
discriminant analysis (LDA), proper dimensionality reduction had a large impact
on performance. Finally, the inclusion of NER features and dictionaries was
found not to help classification.Comment: Pacific Symposium on Biocomputing, 201
Cell line name recognition in support of the identification of synthetic lethality in cancer from text
Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus.
Results: We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers
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