ReviewDatabase : the journal of biological databases and curation2016
Text mining resources for the life sciences.
Review in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Potential and Limitations of Large Language Models for Medical Literature Analysis: A Preliminary Investigation.Cureus · 2025Article
- Review
- Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.Computational and structural biotechnology journal · 2025Article
- Classification of periodontitis stage and grade using natural language processing techniques.PLOS digital health · 2024Article
- Exploratory bibliometric analysis and text mining to reveal research trends in cardiac aging.Aging medicine (Milton (N.S.W)) · 2024Article
- The landscape of miRNA-mRNA regulatory network and cellular sources in inflammatory bowel diseases: insights from text mining and single cell RNA sequencing analysis.Frontiers in immunology · 2024Article
- MantaID: a machine learning-based tool to automate the identification of biological database IDs.Database : the journal of biological databases and curation · 2023Article
- Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining.Biomolecules · 2022Article
- Knowledge-based approaches to drug discovery for rare diseases.Drug discovery today · 2022Review
- Contribution of radiation education to anxiety reduction among Fukushima Daiichi Nuclear Power Plant workers: a cross sectional study using a text mining method.Journal of radiation research · 2022Article
- OnTheFlyNAR genomics and bioinformatics · 2021Article
- Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence.Journal of personalized medicine · 2021Article
- Towards a Consensus on Alzheimer's Disease Comorbidity?Journal of clinical medicine · 2021Review
- SARSCOVIDB-A New Platform for the Analysis of the Molecular Impact of SARS-CoV-2 Viral Infection.ACS omega · 2021Article
- Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis.Frontiers in cell and developmental biology · 2021Article
- Common Genes Involved in Autophagy, Cellular Senescence and the Inflammatory Response in AMD and Drug Discovery Identified via Biomedical Databases.Translational vision science & technology · 2021Article
- ZIKAVID-Zika virus infection database: a new platform to analyze the molecular impact of Zika virus infection.Journal of neurovirology · 2020Article
- Drug Abuse Research Trend Investigation with Text Mining.Computational and mathematical methods in medicine · 2020Article
- Integrative Analysis of Membrane Proteome and MicroRNA Reveals Novel Lung Cancer Metastasis Biomarkers.Frontiers in genetics · 2020Article
- A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems.Journal of healthcare engineering · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable-those that have the crucial ability to share information, enabling smooth integration and reusability.
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.