ArticleDatabase : the journal of biological databases and curation2019
Building deep learning models for evidence classification from the open access biomedical literature.
Article in Database : the journal of biological databases and curation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- DMLS: an automated pipeline to extract the Drosophila modular transcription regulators and targets from massive literature articles.Database : the journal of biological databases and curation · 2024Article
- MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.BMC bioinformatics · 2024Article
- Automatic identification of scientific publications describing digital reconstructions of neural morphology.Brain informatics · 2023Article
- Automatic identification of scientific publications describing digital reconstructions of neural morphology.bioRxiv : the preprint server for biology · 2023Article
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- YTLR: Extracting yeast transcription factor-gene associations from the literature using automated literature readers.Computational and structural biotechnology journal · 2022Article
- Utilizing image and caption information for biomedical document classification.Bioinformatics (Oxford, England) · 2021Article
- Towards a unified open access dataset of molecular interactions.Nature communications · 2020Review
- SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts.Nucleic acids research · 2020Article
- Integrating image caption information into biomedical document classification in support of biocuration.Database : the journal of biological databases and curation · 2020Article
- UPCLASS: a deep learning-based classifier for UniProtKB entry publications.Database : the journal of biological databases and curation · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We investigate the application of deep learning to biocuration tasks that involve classification of text associated with biomedical evidence in primary research articles. We developed a large-scale corpus of molecular papers derived from PubMed and PubMed Central open access records and used it to train deep learning word embeddings under the GloVe, FastText and ELMo algorithms. We applied those models to a distant supervised method classification task based on text from figure captions or fragments surrounding references to figures in the main text using a variety or models and parameterizations. We then developed document classification (triage) methods for molecular interaction papers by using deep learning mechanisms of attention to aggregate classification-based decisions over selected paragraphs in the document. We were able to obtain triage performance with an accuracy of 0.82 using a combined convolutional neural network, bi-directional long short-term memory architecture augmented by attention to produce a single decision for triage. In this work, we hope to encourage biocuration systems developers to apply deep learning methods to their specialized tasks by repurposing large-scale word embedding to apply to their data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.