ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018
Deep Learning data integration for better risk stratification models of bladder cancer.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integration.Bioinformatics (Oxford, England) · 2025Article
- Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology.Radiation oncology (London, England) · 2024Article
- MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder.BioData mining · 2024Article
- Bladder Cancer and Artificial Intelligence: Emerging Applications.The Urologic clinics of North America · 2024Review
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.Journal of cancer research and clinical oncology · 2023Article
- CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data.Research square · 2023Article
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- Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes.Bioinformatics advances · 2023Article
- Multimodal analysis methods in predictive biomedicine.Computational and structural biotechnology journal · 2023Review
- A benchmark study of deep learning-based multi-omics data fusion methods for cancer.Genome biology · 2022Article
- Future directions for precision oncology in prostate cancer.The Prostate · 2022Review
- Multimodal deep learning for biomedical data fusion: a review.Briefings in bioinformatics · 2022Review
- Harnessing multimodal data integration to advance precision oncology.Nature reviews. Cancer · 2022Review
- An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.Computational and mathematical methods in medicine · 2022Article
- SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition.Frontiers in genetics · 2022Article
- Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- Novel deep learning-based solution for identification of prognostic subgroups in liver cancer (Hepatocellular carcinoma).BMC bioinformatics · 2021Article
- A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We propose an unsupervised multi-omics integration pipeline, using deep-learning autoencoder algorithm, to predict the survival subtypes in bladder cancer (BC). We used TCGA dataset comprising mRNA, miRNA and methylation to infer two survival subtypes. We then constructed a supervised classification model to predict the survival subgroups of any new individual sample. Our training data gave two subgroups with significant survival differences (p-value=8e-4), where high-risk survival subgroup was enriched with KRT6/14 overexpression and PI3K-Akt pathways. We tested the robustness of model by randomly splitting the main dataset into multiple training and test folds, which gave overall significant p-values. Then, we successfully inferred the subtypes for a subset of samples kept as test dataset (p-value=0.03). We further applied our pipeline to predict the survival subgroups from another validation dataset with miRNA data (p-value=0.02). Conclusively, present pipeline is an effective approach to infer the survival subtype of a new sample, exemplified by BC.
Identifiers
29888072PMC5961799What 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.