ArticleBriefings in bioinformatics2023
Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 17 citations in OpenAlex.
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.NAR genomics and bioinformatics · 2026Article
- Benchmarking computational methods for multi-omics biomarker discovery in cancer.Briefings in bioinformatics · 2026Article
- Plasma proteomics for Parkinson's disease classification: cross-cohort benchmarking of proteomic, transcriptomic, and multimodal models.NPJ Parkinson's disease · 2026Article
- Robust graph structure learning to improve multi-omics cancer subtype classification.BMC bioinformatics · 2026Article
- Article
- Article
- Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence.Biomedicines · 2026Article
- Knowledge-guided graph fusion of mRNA profiles for interpretable cancer subtyping.Briefings in functional genomics · 2026Article
- Explainable deep learning approaches and clinical insights for cancer biomarker identification.Frontiers in oncology · 2026Review
- AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.Clinical and experimental medicine · 2025Review
- Adaptive multi-omics integration framework for breast cancer survival analysis.Scientific reports · 2025Article
- MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.Briefings in bioinformatics · 2025Article
- DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification.BMC genomics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment.
Indexed as
Identifiers
What Socratic 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 Socratic graph.