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Extracting chemical–protein relations using attention-based neural networks

https://doi.org/10.1093/database/bay102
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Biomedical text mining and its applications in cancer research
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Manual curation is not sufficient for annotation of genomic databases
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Deep learning with word embeddings improves biomedical named entity recognition
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Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task
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CD-REST: a system for extracting chemical-induced disease relation in literature
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RAIN: RNA–protein Association and Interaction Networks
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Chemical-induced disease relation extraction via convolutional neural network
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Biomedical named entity recognition based on extended Recurrent Neural Networks
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BANNER: AN EXECUTABLE SURVEY OF ADVANCES IN BIOMEDICAL NAMED ENTITY RECOGNITION
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In Silico Investigation into Cellular Mechanisms of Cardiac Alternans in Myocardial Ischemia
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Unsupervised Multilingual Sentence Boundary Detection
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The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text
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A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems
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miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases
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Community-Level Income Inequality and HIV Prevalence among Persons Who Inject Drugs in Thai Nguyen, Vietnam
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Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers
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MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications
inferredcites →https://doi.org/10.18653/v1/w17-2304openalex-snapshot
Deep learning for extracting protein-protein interactions from biomedical literature
inferredcites →https://doi.org/10.18653/v1/w17-2315openalex-snapshot
Biomedical Event Extraction using Abstract Meaning Representation
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BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations
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NLTK
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
inferredcites →https://doi.org/10.3115/v1/n15-1openalex-snapshot
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
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Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature
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Additional file 1 of Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts
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