ArticleBriefings in bioinformatics2024
DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women.Briefings in bioinformatics · 2026Article
- Harnessing human tumor organoids for cancer modeling and precision therapy.Protein & cell · 2026Review
- FastMI-HGNet: A Two-Stream Heterogeneous Graph Neural Network for Multi-Omics Disease Classification.Genes · 2026Article
- Sparsity is all you need: rethinking biologically informed neural networks.Briefings in bioinformatics · 2026Article
- OmicsTransformer: self-supervised masked consistency and uncertainty-aware fusion for robust multi-omics prediction.Bioinformatics (Oxford, England) · 2026Article
- PIMO: pathway-based interpretable multiomics interactions for multiomics integration.Bioinformatics (Oxford, England) · 2026Article
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- Epigenetic biomarkers in inflammatory bowel diseases-computational challenges and opportunities.Journal of Crohn's & colitis · 2026Review
- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- Benchmarking computational methods for multi-omics biomarker discovery in cancer.Briefings in bioinformatics · 2026Article
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Plasma proteomics for Parkinson's disease classification: cross-cohort benchmarking of proteomic, transcriptomic, and multimodal models.NPJ Parkinson's disease · 2026Article
- MOGANet: A Multi-omics Graph Attention Network for Cancer Diagnosis and Biomarker Identification.Interdisciplinary sciences, computational life sciences · 2026Article
- MultiGEOmics: Graph-Based Integration of Multi-Omics via Biological Information Flows.bioRxiv : the preprint server for biology · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- An unsupervised method for spatial transcriptomics analysis based on adversarial autoencoder.Briefings in bioinformatics · 2026Article
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Intra- and inter-multi-omics interaction analysis using deep learning.Bioinformatics advances · 2026Article
- MiracleNet: A Biologically Interpretable Machine Learning Model for Resected Non-small-cell Lung Cancer.Computational and structural biotechnology journal · 2026Article
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Authors and funding
6 authors.
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Abstract
Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.
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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.