ArticlePloS one2014
Machine learning for biomedical literature triage.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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Who cites it
16 citing papers in PubMed, 40 citations in OpenAlex.
- 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
- Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases.Diagnostics (Basel, Switzerland) · 2022Article
- Utilizing image and caption information for biomedical document classification.Bioinformatics (Oxford, England) · 2021Article
- Deep Learning of Histopathology Images at the Single Cell Level.Frontiers in artificial intelligence · 2021Review
- Information Retrieval Using Machine Learning for Biomarker Curation in the Exposome-Explorer.Frontiers in research metrics and analytics · 2021Article
- Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms.Journal of healthcare engineering · 2021Review
- Biomedical document triage using a hierarchical attention-based capsule network.BMC bioinformatics · 2020Article
- Integrating image caption information into biomedical document classification in support of biocuration.Database : the journal of biological databases and curation · 2020Article
- Computer-aided diagnosis of external and middle ear conditions: A machine learning approach.PloS one · 2020Article
- Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer.Frontiers in genetics · 2020Article
- Machine learning approach to literature mining for the genetics of complex diseases.Database : the journal of biological databases and curation · 2019Article
- An effective biomedical document classification scheme in support of biocuration: addressing class imbalance.Database : the journal of biological databases and curation · 2019Article
- Scaling up data curation using deep learning: An application to literature triage in genomic variation resources.PLoS computational biology · 2018Article
- Hierarchical bi-directional attention-based RNNs for supporting document classification on protein-protein interactions affected by genetic mutations.Database : the journal of biological databases and curation · 2018Article
- mycoCLAP, the database for characterized lignocellulose-active proteins of fungal origin: resource and text mining curation support.Database : the journal of biological databases and curation · 2015Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.
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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.