ReviewExpert review of clinical pharmacology2014
Big data: the next frontier for innovation in therapeutics and healthcare.
Review in Expert review of clinical pharmacology, 2014. 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.
- Plasma proteomics for Parkinson's disease classification: cross-cohort benchmarking of proteomic, transcriptomic, and multimodal models.NPJ Parkinson's disease · 2026Article
- A strategic framework for data science integration in undergraduate medical education and clinical training.Journal of education and health promotion · 2026Review
- High-dimensional biomarker identification for interpretable disease prediction via machine learning models.Bioinformatics (Oxford, England) · 2025Article
- Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.Gastroenterology and hepatology from bed to bench · 2024Review
- An Integrated Platform for Skin Cancer Heterogenous and Multilayered Data Management.Journal of medical systems · 2021Article
- Research on the Construction and Application of Breast Cancer-Specific Database System Based on Full Data Lifecycle.Frontiers in public health · 2021Article
- The use or generation of biomedical data and existing medicines to discover and establish new treatments for patients with rare diseases - recommendations of the IRDiRC Data Mining and Repurposing Task Force.Orphanet journal of rare diseases · 2019Article
- Antibiotic modulation of mucins in otitis media; should this change our approach to watchful waiting?International journal of pediatric otorhinolaryngology · 2019Article
- Creation of a Multicenter Pediatric Inpatient Data Repository Derived from Electronic Health Records.Applied clinical informatics · 2019Article
- Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System.Journal of clinical medicine · 2019Review
- A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.Healthcare (Basel, Switzerland) · 2018Review
- Treatment decisions in multiple sclerosis - insights from real-world observational studies.Nature reviews. Neurology · 2017Review
- Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.Gastroenterology · 2017Review
- A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries.International journal of genomics · 2017Review
- Challenges and Opportunities of Big Data in Health Care: A Systematic Review.JMIR medical informatics · 2016Review
- Transforming Scientific Inquiry: Tapping Into Digital Data by Building a Culture of Transparency and Consent.Academic medicine : journal of the Association of American Medical Colleges · 2016Article
- A crowdsourcing workflow for extracting chemical-induced disease relations from free text.Database : the journal of biological databases and curation · 2016Article
- Conceptualizing type 2 diabetes and its management.Journal of multidisciplinary healthcare · 2016Review
- Public health evolutionary biology of antimicrobial resistance: priorities for intervention.Evolutionary applications · 2015Article
- Quo vadis computational analysis of PPI data or why the future isn't here yet.Frontiers in genetics · 2015Article
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
Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the '-omics', wherein an individual's genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into 'big data' informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. 'Big data' informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient '-omics' data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of 'big data' informatics for clinical pharmacology.
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.