ReviewJournal of personalized medicine2024
Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine.
Review in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- The role of gut microbiome in aging-associated diseases: where do we stand now and how technology will transform the future.Gut microbes · 2026Review
- A four‑gene signature identifies TKI‑induced persister cells and uncovers a ZLN005‑induced pyroptotic vulnerability via the GSDME pathway in EGFR‑mutant lung cancer.Molecular medicine reports · 2026Article
- From aging biology to cardiac biotechnology: emerging platforms for modeling cardiac aging.JCI insight · 2026Review
- Machine Learning in Nonhuman Primate Models of Infectious Diseases: Current Applications and Future Perspectives.Journal of the American Association for Laboratory Animal Science : JAALAS · 2026Review
- Postbiotics and paraprobiotics in food biochemistry mechanisms stability and nutritional applications.NPJ science of food · 2026Review
- Skin Organoids in Proteostasis Research: Early Insights into Aging.Biomolecules & therapeutics · 2026Review
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- Beyond the diagnostic threshold: paradigm shift in laboratory medicine from passive reaction to predictive capability.Frontiers in medicine · 2026Article
- Mechanistic redundancy and hierarchy of aging mechanisms: implications for strategies to extend healthspan and biomarker integration.Frontiers in aging · 2026Review
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Nanoparticle-Based Tools to Study Hallmarks of Aging at the Molecular Level.International journal of nanomedicine · 2026Review
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- Therapy-Induced Senescence (TIS) and SASP: The p53-Mediated Interplay in Cancer Progression and Treatment.International journal of molecular sciences · 2025Review
- From Pathophysiology to Innovative Therapies in Eye Diseases: A Brief Overview.International journal of molecular sciences · 2025Review
- The emerging role of multiomics in aging research.Epigenomics · 2025Review
- Artificial Intelligence in Assessing Reproductive Aging: Role of Mitochondria, Oxidative Stress, and Telomere Biology.Diagnostics (Basel, Switzerland) · 2025Review
- Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins.Biophysical reviews · 2025Review
- Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.Journal of clinical medicine · 2025Review
- Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer.Journal of personalized medicine · 2025Review
- Personalized Stem Cell-Based Regeneration in Spinal Cord Injury Care.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies-including genomics, transcriptomics, epigenomics, proteomics, and metabolomics-in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy.
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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.