ArticlePLoS computational biology2018
A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it, 168 citations in OpenAlex.
- Machine learning for multi-omics data integration in crop improvement: a systematic review.BMC bioinformatics · 2026Pooled it
- Article
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery.Nucleic acids research · 2025Article
- Socio-environmental modeling shows physics-like confidence with water modeling surpassing it in numerical claims.iScience · 2025Article
- Unlocking the potential of PubMed Central supplementary data files.Bioinformatics advances · 2025Article
- Protein swarm-based cause-effect analysis: effects of microRNAs on cooperation networks linking COVID-19 infections, atherosclerosis, and Alzheimer's disease.Frontiers in cardiovascular medicine · 2025Article
- EnzChemRED, a rich enzyme chemistry relation extraction dataset.Scientific data · 2024Article
- Improving dictionary-based named entity recognition with deep learning.Bioinformatics (Oxford, England) · 2024Article
- PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.Nucleic acids research · 2024Article
- Decoding hotline's information with text-mining: A protocol for improving tobacco control in Shanghai.Tobacco induced diseases · 2024Article
- The application of natural language processing for the extraction of mechanistic information in toxicology.Frontiers in toxicology · 2024Article
- Unsupervised learning and natural language processing highlight research trends in a superbug.Frontiers in artificial intelligence · 2024Article
- Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms.Scientific data · 2023Article
- Drug dosage modifications in 24 million in-patient prescriptions covering eight years: A Danish population-wide study of polypharmacy.PLOS digital health · 2023Article
- BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus.PLoS computational biology · 2023Article
- Automated assembly of molecular mechanisms at scale from text mining and curated databases.Molecular systems biology · 2023Article
- Nanopublication-based semantic publishing and reviewing: a field study with formalization papers.PeerJ. Computer science · 2023Article
- Emati: a recommender system for biomedical literature based on supervised learning.Database : the journal of biological databases and curation · 2022Article
- e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.Briefings in bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.
Indexed as
Identifiers
What OpenQuestion holds
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.