ReviewJournal of translational medicine2025
Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.
Review in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Tunable acoustic rotation for deep biophysical phenotyping of preclinical Alzheimer's disease.Materials today. Bio · 2026Article
- Review
- Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery.Briefings in bioinformatics · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Cross-Generational Integration of Exercise and Nutritional Encoding in Offspring Adipose Genomics.International journal of molecular sciences · 2026Review
- Review
- Advances in Functional and Metabolic Imaging for Early Tumor Treatment Response and Resistance Evaluation: A Review.Cancers · 2026Review
- Challenges and Opportunities in Multi-Omics Data Acquisition and Analysis: Toward Integrative Solutions.Biomolecules · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Clonal evolution in gastrointestinal cancers: multi-omics insights into tumor heterogeneity, microenvironmental selection, and translational biomarkers.Frontiers in oncology · 2026Review
- Editorial: Artificial intelligence in multi-omics: advancing tumor metastasis prediction and mechanism analysis.Frontiers in cell and developmental biology · 2026Article
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
Abstract
Recent advances in single-cell multi-omics technologies have revolutionized cellular analysis, enabling comprehensive exploration of cellular heterogeneity, developmental trajectories, and disease mechanisms at unprecedented resolution. Foundation models, originally developed for natural language processing, are now driving transformative approaches to high-dimensional, multimodal single-cell data analysis. Frameworks such as scGPT and scPlantFormer excel in cross-species cell annotation, in silico perturbation modeling, and gene regulatory network inference. Multimodal integration approaches, including pathology-aligned embeddings and tensor-based fusion, harmonize transcriptomic, epigenomic, proteomic, and spatial imaging data to delineate multilayered regulatory networks across biological scales. Federated computational platforms facilitate decentralized data analysis and standardized, reproducible workflows, fostering global collaboration. Challenges persist, including technical variability across platforms, limited model interpretability, and gaps in translating computational insights into clinical applications. Overcoming these hurdles demands standardized benchmarking, multimodal knowledge graphs, and collaborative frameworks that integrate artificial intelligence with human expertise. This review synthesizes recent technological advancements and proposes actionable strategies to bridge single-cell multi-omics innovations with mechanistic biology and precision medicine.
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.