ArticleComputational and mathematical methods in medicine2022
An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Inflammatory marker-driven deep learning model for postoperative gastric cancer prognosis.BMC medical informatics and decision making · 2026Article
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Computational identification and validation of non-coding rna biomarkers in gastrointestinal cancer.Functional & integrative genomics · 2025Review
- Unsupervised Clustering Successfully Predicts Prognosis in NSCLC Brain Metastasis Cohorts.Diagnostics (Basel, Switzerland) · 2025Article
- Immune-related genes can accurately predict survival in bladder cancer: a retrospective study via two independent immunotherapy cohorts.Translational andrology and urology · 2025Article
- Gastric cancer in children infected withWorld journal of gastrointestinal oncology · 2025Article
- Navigating Cancer Complexity: Integrative Multi-Omics Methodologies for Clinical Insights.Clinical Medicine Insights. Oncology · 2025Review
- Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence.BMC cancer · 2024Article
- Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare.Biomedicines · 2024Review
- Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer.International journal of molecular sciences · 2024Review
- Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer.Current medicinal chemistry · 2024Review
- A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.Translational cancer research · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Methods: Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the Results: The patients were divided into low-risk and high-risk groups according to the Conclusions: A robust and generalizable model based on the autoencoder was proposed to integrate multiomics data and predict the prognosis of patients with stomach adenocarcinoma. The model demonstrates better performance than two alternative approaches on prognosis prediction. The results might provide the grounds for further exploring the potential biomarkers to predict the prognosis of patients with stomach adenocarcinoma.
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