ArticleJournal, genetic engineering & biotechnology2026
AI based multiomics integration for cancer diagnosis and prognosis.
Article in Journal, genetic engineering & biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer remains a critical global health challenge, driving the need for innovative approaches in diagnosis and treatment. This research introduces OmicsFusionNet, an AI-powered hybrid model integrating machine learning and deep learning to revolutionize cancer care. The tool incorporates up to six multiomics datasets-genomics, transcriptomics, and epigenomics-achieving 80.2% accuracy across 23 cancer types. Notably, RNAseq and methylation integration reached 99.8% accuracy, highlighting XGBoost's feature selection and deep learning's classification strength. For ovarian cancer stage detection, OmicsFusionNet optimized analysis using CPTAC-OV and TCGA-OV datasets, achieving accuracies between 83% and 91% by combining ElasticNet and XGBoost with deep learning. Additionally, KEGG pathway enrichment of multiomics biomarkers identified key cancer-related pathways, advancing early detection, biomarker discovery, and personalized treatments. This study underscores the transformative potential of AI and multiomics integration in cancer research, enabling precise interventions and uncovering novel mechanisms that enhance patient outcomes.
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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.