ArticleBioinformatics (Oxford, England)2024
Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women.Briefings in bioinformatics · 2026Article
- Pathway potency in methylation altered network reveals lung cancer branching evolution and hallmarks.iScience · 2026Article
- Biologically informed neural network models are robust to spurious interactions via self-pruning.Bioinformatics (Oxford, England) · 2026Article
- Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation.Bioinformatics (Oxford, England) · 2026Article
- Sparsity is all you need: rethinking biologically informed neural networks.Briefings in bioinformatics · 2026Article
- PIMO: pathway-based interpretable multiomics interactions for multiomics integration.Bioinformatics (Oxford, England) · 2026Article
- Bridging Ancestry-Stratified Bias in Pharmacogenomics AI: Toward Metabolomics-Inclusive Multi-Omics Precision Medicine.Journal of personalized medicine · 2026Review
- Benchmarking computational methods for multi-omics biomarker discovery in cancer.Briefings in bioinformatics · 2026Article
- IST: an ontology-guided attention-based autoencoder for interpretable analysis of single-cell transcriptomic data.Scientific reports · 2026Article
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- CeLLTra: aligning cell names with gene expression via a pathway-informed transformer.Bioinformatics (Oxford, England) · 2026Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Biologically informed neural network models are robust to spurious interactions via self-pruning.bioRxiv : the preprint server for biology · 2025Article
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
- Multi-Omics Feature Selection to Identify Biomarkers for Hepatocellular Carcinoma.Metabolites · 2025Article
- Oxidative Stress and Inflammation in Hypoxemic Respiratory Diseases and Their Comorbidities: Molecular Insights and Diagnostic Advances in Chronic Obstructive Pulmonary Disease and Sleep Apnea.Antioxidants (Basel, Switzerland) · 2025Review
- Integrative Analysis of Multi-Omics Data for Biomarker Discovery.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Integrative Multi-Omics and Routine Blood Analysis Using Deep Learning: Cost-Effective Early Prediction of Chronic Disease Risks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Review
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
motivationMulti-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability.
resultsTo integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study. AVAILABILITY AND IMPLEMENTATION: Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.
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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.