ReviewQuantitative biology (Beijing, China)2025
Revolutionizing multi-omics analysis with artificial intelligence and data processing.
Review in Quantitative biology (Beijing, China), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 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
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.Journal of nanobiotechnology · 2026Pooled it
- The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research.Microorganisms · 2026Review
- Decoding neuro-tumor interactions in pancreatic cancer: mechanisms, immunosuppressive networks and therapeutic opportunities.Molecular biomedicine · 2026Review
- Insights into Microbiota-Vaccine Crosstalk in Humans: Mechanisms, Modulators, and Translational Horizons.Vaccines · 2026Review
- Review
- Artificial Intelligence-Powered Histopathology in Stem Cell Research: Bridging Morphology, Function, and Omics.Current issues in molecular biology · 2026Review
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- Dopamine Receptor D2 gene Polymorphisms rs2005313, rs4274224, and rs4938019 in Pakistani Patients with Schizophrenia:a Diagnostic Tool for Schizophrenia.Journal of molecular neuroscience : MN · 2026Article
- Neurons Die Not by One Hit, but by Signaling Convergence.Molecular neurobiology · 2026Review
- Multi-Omics-Guided Design and Safety Engineering of Nucleic Acid Therapeutics: From Molecular Perturbation to Predictive Toxicology and Precision Translation.Chemical biology & drug design · 2026Review
- Integrating Molecular Pathology, Tumor Microenvironment, and Novel Therapies to Overcome Resistance in Glioblastoma.Journal of molecular neuroscience : MN · 2026Review
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Integrating CRISPR genome editing with liver organoid and hiPSC-derived microfluidic platforms to model metabolic dysfunction-associated steatotic liver disease.Biochemistry and biophysics reports · 2026Review
- Extracellular vesicles in prostate cancer: current understanding and future perspectives.Journal of the National Cancer Center · 2026Review
- Chemical Epigenetics: Small Molecules Targeting Chromatin Modifiers in Disease Modulation.Cell biochemistry and biophysics · 2026Review
- Quorum Sensing and Quorum Quenching in Periodontal Disease: Mechanisms and Therapeutic Potential.Current issues in molecular biology · 2026Review
- Review
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- The BHARAT study: a multi-modal, multi-omics investigation of aging signatures in the Indian population.Aging · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our understanding of intricate biological systems has been completely transformed by the development of multi-omics approaches, which entail the simultaneous study of several different molecular data types. However, there are many obstacles to overcome when analyzing multi-omics data, including the requirement for sophisticated data processing and analysis tools. The integration of multi-omics research with artificial intelligence (AI) has the potential to fundamentally alter our understanding of biological systems. AI has emerged as an effective tool for evaluating complicated data sets. The application of AI and data processing techniques in multi-omics analysis is explored in this study. The present study articulates the diverse categories of information generated by multi-omics methodologies and the intricacies involved in managing and merging these datasets. Additionally, it looks at the various AI techniques-such as machine learning, deep learning, and neural networks-that have been created for multi-omics analysis. The assessment comes to the conclusion that multi-omics analysis has a lot of potential to change with the integration of AI and data processing techniques. AI can speed up the discovery of new biomarkers and therapeutic targets as well as the advancement of personalized medicine strategies by enabling the integration and analysis of massive and complicated data sets. The necessity for high-quality data sets and the creation of useful algorithms and models are some of the difficulties that come with using AI in multi-omics study. In order to fully exploit the promise of AI in multi-omics analysis, more study in this area is required.
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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.