ReviewGastroenterology2017
Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.
Review in Gastroenterology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Current and Investigational Biomarkers of Liver Disease in HIV-HBV Co-infection.Current HIV/AIDS reports · 2025Review
- Decoding hepatobiliary-specific immune gene patterns in gastrointestinal cancers via gene ontology fingerprints, multi-omics, and experimental integration.Precision clinical medicine · 2025Article
- Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics-Part I: Why Is It Needed?Digestive diseases and sciences · 2024Article
- Clinico-histological and molecular features of hepatocellular carcinoma from nonalcoholic fatty liver disease.Cancer science · 2023Review
- OTTM: an automated classification tool for translational drug discovery from omics data.Briefings in bioinformatics · 2023Article
- From Reductionistic Approach to Systems Immunology Approach for the Understanding of Tumor Microenvironment.International journal of molecular sciences · 2023Review
- A knowledge empowered explainable gene ontology fingerprint approach to improve gene functional explication and prediction.iScience · 2023Article
- Hepatocellular Carcinoma Chemoprevention with Generic Agents.Seminars in liver disease · 2022Review
- A Quantitative Systems Pharmacology Platform Reveals NAFLD Pathophysiological States and Targeting Strategies.Metabolites · 2022Article
- Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.International journal of molecular sciences · 2022Review
- Characterizing patients with rare mucormycosis infections using real-world data.BMC infectious diseases · 2022Article
- Hepatic fibrosis 2022: Unmet needs and a blueprint for the future.Hepatology (Baltimore, Md.) · 2022Review
- A Review of the Role and Challenges of Big Data in Healthcare Informatics and Analytics.Computational intelligence and neuroscience · 2022Review
- Quantifying the progression of non-alcoholic fatty liver disease in human biomimetic liver microphysiology systems with fluorescent protein biosensors.Experimental biology and medicine (Maywood, N.J.) · 2021Review
- Repositioning of a novel GABA-B receptor agonist, AZD3355 (Lesogaberan), for the treatment of non-alcoholic steatohepatitis.Scientific reports · 2021Article
- Article
- Review
- Targeting Non-Oncogene Addiction for Cancer Therapy.Biomolecules · 2021Review
- Big data in systemic sclerosis: Great potential for the future.Journal of scleroderma and related disorders · 2020Review
- Omics-derived hepatocellular carcinoma risk biomarkers for precision care of chronic liver diseases.Hepatology research : the official journal of the Japan Society of Hepatology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.
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.