ArticleExpert review of precision medicine and drug development2019
Use of big data in drug development for precision medicine: an update.
Article in Expert review of precision medicine and drug development, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recommendations for robust and reproducible preclinical research in personalised medicine.BMC medicine · 2023Guideline
- Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications.Diagnostics (Basel, Switzerland) · 2025Review
- Big data for neuroscience in the context of predictive, preventive, and personalized medicine.The EPMA journal · 2025Review
- Early and hereditary breast cancer: advances in risk stratification and imaging approaches.Therapeutic advances in medical oncology · 2025Review
- Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure.BMC bioinformatics · 2024Article
- Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective.Brain sciences · 2024Article
- Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.ACS chemical neuroscience · 2024Article
- Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease.Biomolecules · 2024Review
- Simple and Scalable Algorithms for Cluster-Aware Precision Medicine.Proceedings of machine learning research · 2024Article
- Identifying and overcoming challenges in the EMA's qualification of novel methodologies: a two-year review.Frontiers in pharmacology · 2024Article
- Investigating the therapeutic potential of hesperidin targeting CRISP2 in intervertebral disc degeneration and cancer risk mitigation.Frontiers in pharmacology · 2024Article
- AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine.Database : the journal of biological databases and curation · 2023Article
- Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Article
- SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Hepatocellular Carcinoma Chemoprevention with Generic Agents.Seminars in liver disease · 2022Review
- Big Data Precision Marketing Approach under IoT Cloud Platform Information Mining.Computational intelligence and neuroscience · 2022Review
- The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.Computational and structural biotechnology journal · 2022Review
- Identification and Development of Therapeutics for COVID-19.mSystems · 2021Article
- Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.Molecular diversity · 2021Article
- Review
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
introductionBig-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios. AREAS COVERED: Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery. EXPERT OPINION: In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.
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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.