ArticleJournal of biomedical informatics2017
Envisioning the future of 'big data' biomedicine.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The ethical and legal landscape of brain data governance.PloS one · 2022Pooled it
- Utilizing Omic Data to Understand Integrative Physiology.Physiology (Bethesda, Md.) · 2025Review
- PEPhub: a database, web interface, and API for editing, sharing, and validating biological sample metadata.bioRxiv : the preprint server for biology · 2024Article
- PEPhub: a database, web interface, and API for editing, sharing, and validating biological sample metadata.GigaScience · 2024Article
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.Nature catalysis · 2024Article
- Editorial: Big data for biomedical research of inflammatory diseases.Frontiers in pharmacology · 2023Article
- P4 Medicine as a model for precision periodontal care.Clinical oral investigations · 2022Review
- Opportunities and Challenges in Democratizing Immunology Datasets.Frontiers in immunology · 2021Review
- Influential Usage of Big Data and Artificial Intelligence in Healthcare.Computational and mathematical methods in medicine · 2021Review
- Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.PLoS biology · 2020Article
- The National Institutes of Health funding for clinical research applying machine learning techniques in 2017.NPJ digital medicine · 2020Article
- Progress in data interoperability to support computational toxicology and chemical safety evaluation.Toxicology and applied pharmacology · 2019Review
- Neurotrauma as a big-data problem.Current opinion in neurology · 2018Review
- mHealth for the Detection and Intervention in Adolescent and Young Adult Substance Use Disorder.Current addiction reports · 2018Article
- Platform for Automated Real-Time High Performance Analytics on Medical Image Data.IEEE journal of biomedical and health informatics · 2018Article
- Recon3D enables a three-dimensional view of gene variation in human metabolism.Nature biotechnology · 2018Article
- Exploring completeness in clinical data research networks with DQe-c.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Article
- Assessing health risks from multiple environmental stressors: Moving from G×E to I×E.Mutation research. Reviews in mutation researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Through the increasing availability of more efficient data collection procedures, biomedical scientists are now confronting ever larger sets of data, often finding themselves struggling to process and interpret what they have gathered. This, while still more data continues to accumulate. This torrent of biomedical information necessitates creative thinking about how the data are being generated, how they might be best managed, analyzed, and eventually how they can be transformed into further scientific understanding for improving patient care. Recognizing this as a major challenge, the National Institutes of Health (NIH) has spearheaded the "Big Data to Knowledge" (BD2K) program - the agency's most ambitious biomedical informatics effort ever undertaken to date. In this commentary, we describe how the NIH has taken on "big data" science head-on, how a consortium of leading research centers are developing the means for handling large-scale data, and how such activities are being marshalled for the training of a new generation of biomedical data scientists. All in all, the NIH BD2K program seeks to position data science at the heart of 21
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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.