ArticleScientific reports2020
Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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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.
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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.
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Who cites it
42 citing papers in PubMed.
- Metabolic pathway signatures define prognostic subtypes of lung adenocarcinoma.Discover oncology · 2026Article
- AI and Big Data in Oncology: A Physician-Centered Perspective on Emerging Clinical and Research Applications.Cancer innovation · 2026Review
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- Unraveling the molecular landscape of pancreatic cancer: a systems biology approach to identify therapeutic targets.Discover oncology · 2025Article
- Review
- Machine learning and gene network integration reveal prognostic subnetworks and biomarkers in pancreatic cancer.Computational and structural biotechnology journal · 2025Article
- Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma.Genes & diseases · 2024Review
- Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence.Cancers · 2024Review
- Basic Principles and Role of Endoscopic Ultrasound in Diagnosis and Differentiation of Pancreatic Cancer from Other Pancreatic Lesions: A Comprehensive Review of Endoscopic Ultrasound for Pancreatic Cancer.Journal of clinical medicine · 2024Review
- GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network.Bioinformatics (Oxford, England) · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Using published pathway figures in enrichment analysis and machine learning.BMC genomics · 2023Article
- Mutational landscape of cancer-driver genes across human cancers.Scientific reports · 2023Article
- Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review.Journal of cancer research and clinical oncology · 2023Review
- A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound.Journal of clinical medicine · 2023Review
- Human pancreatic cancer patients with Epithelial-to-Mesenchymal Transition and an aggressive phenotype show a disturbed balance in Protein Phosphatase Type 2A expression and functionality.Journal of translational medicine · 2023Article
- Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review.Journal of medical Internet research · 2023Article
- High-throughput functional annotation of natural products by integrated activity profiling.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Scalable transcriptomics analysis with Dask: applications in data science and machine learning.BMC bioinformatics · 2022Review
- Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions.Biomimetics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To address this challenge, we used targeted proteomics and other molecular data compiled by The Cancer Genome Atlas to reveal that pancreatic tumours can be broadly segregated into two distinct subtypes. Besides being associated with substantially different clinical outcomes, tumours belonging to each of these subtypes also display notable differences in diverse signalling pathways and biological processes. At the proteome level, we show that tumours belonging to the less severe subtype are characterised by aberrant mTOR signalling, whereas those belonging to the more severe subtype are characterised by disruptions in SMAD and cell cycle-related processes. We use machine learning algorithms to define sets of proteins, mRNAs, miRNAs and DNA methylation patterns that could serve as biomarkers to accurately differentiate between the two pancreatic cancer subtypes. Lastly, we confirm the biological relevance of the identified biomarkers by showing that these can be used together with pattern-recognition algorithms to accurately infer the drug sensitivity of pancreatic cancer cell lines. Our study shows that integrative profiling of multiple data types enables a biological and clinical representation of pancreatic cancer that is comprehensive enough to provide a foundation for future therapeutic strategies.
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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.