ArticleDatabase : the journal of biological databases and curation2018
iTextMine: integrated text-mining system for large-scale knowledge extraction from the literature.
Article in Database : the journal of biological databases and curation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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Who cites it
14 citing papers in PubMed.
- HERMES: an open-source mining tool for open-access literature.Bioinformatics advances · 2026Article
- Characterization and automated classification of sentences in the biomedical literature: a case study for biocuration of gene expression and protein kinase activity.Database : the journal of biological databases and curation · 2025Article
- Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases.Annals of medicine · 2023Article
- KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.PeerJ · 2023Article
- Graph data science and machine learning for the detection of COVID-19 infection from symptoms.PeerJ. Computer science · 2023Article
- Schwann Cells Induce Phenotypic Changes in Oral Cancer Cells.Advanced biology · 2022Article
- Automated extraction of genes associated with antibiotic resistance from the biomedical literature.Database : the journal of biological databases and curation · 2022Article
- An annotated dataset for extracting gene-melanoma relations from scientific literature.Journal of biomedical semantics · 2022Article
- COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases.Bioinformatics (Oxford, England) · 2021Article
- Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types.Journal of translational medicine · 2021Article
- emiRIT: a text-mining-based resource for microRNA information.Database : the journal of biological databases and curation · 2021Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- ScanBious: Survey for Obesity Genes Using PubMed Abstracts and DisGeNET.Journal of personalized medicine · 2021Article
- Peryton: a manual collection of experimentally supported microbe-disease associations.Nucleic acids research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Numerous efforts have been made for developing text-mining tools to extract information from biomedical text automatically. They have assisted in many biological tasks, such as database curation and hypothesis generation. Text-mining tools are usually different from each other in terms of programming language, system dependency and input/output format. There are few previous works that concern the integration of different text-mining tools and their results from large-scale text processing. In this paper, we describe the iTextMine system with an automated workflow to run multiple text-mining tools on large-scale text for knowledge extraction. We employ parallel processing with dockerized text-mining tools with a standardized JSON output format and implement a text alignment algorithm to solve the text discrepancy for result integration. iTextMine presently integrates four relation extraction tools, which have been used to process all the Medline abstracts and PMC open access full-length articles. The website allows users to browse the text evidence and view integrated results for knowledge discovery through a network view. We demonstrate the utilities of iTextMine with two use cases involving the gene PTEN and breast cancer and the gene SATB1.
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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.