ArticleBioinformatics (Oxford, England)2022
MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- FastMI-HGNet: A Two-Stream Heterogeneous Graph Neural Network for Multi-Omics Disease Classification.Genes · 2026Article
- A powerful representation learning method for enhanced analysis of incomplete multi-omics data.NPJ systems biology and applications · 2026Article
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- X-intNMF: a cross- and intra-omics regularized NMF framework for multi-omics integration.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis.Biomedicines · 2026Review
- ULSL: Unified Latent and Similarity Learning for robust multi-omics cancer subtype identification.Bioinformatics advances · 2026Article
- ClinIAN: Clinically Informed Attention Network.Bioinformatics advances · 2026Article
- Digital pathology imaging artificial intelligence in cancer research and clinical trials: An NCI workshop report.Journal of pathology informatics · 2026Review
- MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery.Fundamental research · 2026Article
- Scalable randomized kernel methods for multiview data integration and prediction with application to Coronavirus disease.Biostatistics (Oxford, England) · 2025Article
- MOGEDN: small-sample cancer subtype classification with encoder-decoder networks for missing-omics recovery and biomarker discovery.Briefings in bioinformatics · 2025Article
- AI-powered programmable virtual humans toward human physiologically-based drug discovery.Drug discovery today · 2025Review
- JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data.bioRxiv : the preprint server for biology · 2025Article
- MODA: a graph convolutional network-based multi-omics integration framework for unraveling hub molecules and disease mechanisms.Briefings in bioinformatics · 2025Article
- CrossAttOmics: multiomics data integration with cross-attention.Bioinformatics (Oxford, England) · 2025Article
- An Attention-Aware Multi-Task Learning Framework Identifies Candidate Targets for Drug Repurposing in Sarcopenia.Journal of cachexia, sarcopenia and muscle · 2025Article
- MMnc: multi-modal interpretable representation for non-coding RNA classification and class annotation.Bioinformatics (Oxford, England) · 2025Article
- Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification.IEEE access : practical innovations, open solutions · 2025Article
- Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.Biology methods & protocols · 2025Review
- AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships.Computational and structural biotechnology journal · 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
2 authors.
Funding
Abstract
motivationAccurate diagnostic classification and biological interpretation are important in biology and medicine, which are data-rich sciences. Thus, integration of different data types is necessary for the high predictive accuracy of clinical phenotypes, and more comprehensive analyses for predicting the prognosis of complex diseases are required.
resultsHere, we propose a novel multi-task attention learning algorithm for multi-omics data, termed MOMA, which captures important biological processes for high diagnostic performance and interpretability. MOMA vectorizes features and modules using a geometric approach and focuses on important modules in multi-omics data via an attention mechanism. Experiments using public data on Alzheimer's disease and cancer with various classification tasks demonstrated the superior performance of this approach. The utility of MOMA was also verified using a comparison experiment with an attention mechanism that was turned on or off and biological analysis. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/dmcb-gist/MOMA. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.
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.