ReviewJournal of human genetics2024
Advances in AI and machine learning for predictive medicine.
Review in Journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 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
86 citing papers in PubMed, 128 citations in OpenAlex.
- Insights into the Complexities of Pharmacotherapy Parameters in Artificial Intelligence Models for Drug Selection, Precision Personalised Medicine and Optimal Therapeutic Outcomes.Pharmaceutics · 2026Review
- Machine Learning and Multimodal Biomarker Discovery in Alzheimer's Disease.Brain sciences · 2026Review
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Plasma metabolomic signatures enable the diagnosis and prognosis of chronic obstructive pulmonary disease.Nature communications · 2026Article
- Algorithmic Prognostication in Female Oncofertility Counseling: Ethical Challenges of Bias, Autonomy, and Predictive Uncertainty.Healthcare (Basel, Switzerland) · 2026Review
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Review Article: Biomarkers in Liver Transplantation for Hepatocellular Carcinoma: Towards Precision Medicine.Alimentary pharmacology & therapeutics · 2026Review
- The potential utility of in-silico approach in identifying phytochemicals against various targets for the management of lung cancer.Discover oncology · 2026Review
- The Gut-Brain-Immune Axis: Multi-Omics Insights into Neurodegenerative and Metabolic Diseases.Cells · 2026Review
- Review
- Rheology-driven penetration dynamics of needle-free jet injection in ex vivo porcine tissue.Drug delivery and translational research · 2026Article
- Mechanisms and precision interventions in sarcopenia and osteoarthritis comorbidity: A narrative review.Journal of orthopaedic translation · 2026Review
- Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and Systematic Review (2015-2025).Pharmaceutics · 2026Review
- Carcinogenic Medications: A Review of Specific Agents and Molecular Mechanisms of Carcinogenesis.Cancer reports (Hoboken, N.J.) · 2026Review
- Freezing of Gait in Parkinson's Disease: A Scoping Review on the Path Towards Real-Time Therapies.Sensors (Basel, Switzerland) · 2026Article
- Cross-Generational Integration of Exercise and Nutritional Encoding in Offspring Adipose Genomics.International journal of molecular sciences · 2026Review
- The Axon as a Self-Modifying Computational System: Autonomous Inference, Adaptive Propagation, and AI-Enabled Mechanistic Insight.International journal of molecular sciences · 2026Review
- Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging.Biosensors · 2026Review
- An online interpretable machine learning model for predicting cardiometabolic multimorbidity risk in patients with type 2 diabetes mellitus.Scientific reports · 2026Article
- Machine Learning Application to Predict Bicycle Ergometer Test Results: a Prospective Cohort Study.Sovremennye tekhnologii v meditsine · 2026Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.
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.