ReviewGenes2021
Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications.
Review in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- From signals to systems: the epigenetic-microbiome-mitochondrial axis in IBD pathogenesis.Gut microbes · 2026Review
- MicroRNA‑microbiome cross‑kingdom networks drive inflammatory bowel disease through dynamic regulatory ecosystems (Review).International journal of molecular medicine · 2026Review
- From tissue to blood: an integrated multi-omics signature identifies fibrogenesis and neutrophil activation as key drivers of ulcerative colitis severity.Frontiers in immunology · 2026Article
- Article
- AQP9 and IFITM1 as drivers of immune infiltration and tumor progression in IBD-associated colorectal cancer: from computational insights to experimental validation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Emerging insights of decoding the genetic blueprint, molecular mechanisms, and future horizons in precision medicine for the treatment of type 2 diabetes.Journal of diabetes and metabolic disorders · 2025Review
- Identifying inflammatory bowel disease subtypes: a comprehensive exploration of transcriptomic data and machine learning-based approaches.Therapeutic advances in gastroenterology · 2025Article
- Inflammatory bowel disease genomics, transcriptomics, proteomics and metagenomics meet artificial intelligence.United European gastroenterology journal · 2024Review
- Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease.World journal of gastroenterology · 2024Article
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Challenges in IBD Research 2024: Precision Medicine.Inflammatory bowel diseases · 2024Review
- Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease.World journal of gastroenterology · 2023Review
- Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach.Medicina (Kaunas, Lithuania) · 2023Article
- Laboratory Data and IBDQ-Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis.Journal of clinical medicine · 2023Article
- Advancing the precision management of inflammatory bowel disease in the era of omics approaches and new technology.World journal of gastroenterology · 2023Review
- Molecular medicine-based IBD treatment strategies-we take it personally!Frontiers in gastroenterology (Lausanne, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.
Indexed as
Identifiers
What Socratic 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 Socratic graph.