ReviewPersonalized medicine2019
Preparing next-generation scientists for biomedical big data: artificial intelligence approaches.
Review in Personalized medicine, 2019. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Network-based analyses of multiomics data in biomedicine.BioData mining · 2025Review
- Adapting data science competencies by role and purpose: Voice AI.Frontiers in digital health · 2025Article
- Integration of unpaired single cell omics data by deep transfer graph convolutional network.PLoS computational biology · 2025Article
- Transcriptomics and epigenetic data integration learning module on Google Cloud.Briefings in bioinformatics · 2024Article
- Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform.BMC medical education · 2023Article
- Article
- Timing errors and temporal uncertainty in clinical databases-A narrative review.Frontiers in digital health · 2022Review
- Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review.JMIR medical education · 2021Article
- DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.Briefings in bioinformatics · 2021Article
- scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.Nature communications · 2021Article
- Unsupervised modeling and genome-wide association identify novel features of allergic march trajectories.The Journal of allergy and clinical immunology · 2021Article
- Gut microbiome, big data and machine learning to promote precision medicine for cancer.Nature reviews. Gastroenterology & hepatology · 2020Review
- Strengthening national nutrition research: rationale and options for a new coordinated federal research effort and authority.The American journal of clinical nutrition · 2020Review
- Integrating Biomedical Informatics Training into Existing High School Curricula.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data are being stored in electronic health records yielding big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making. We end with some specific training recommendations for preparing next-generation scientists for biomedical big data.
Indexed as
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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.