ArticleBioData mining2020
Deep learning-based ovarian cancer subtypes identification using multi-omics data.
Article in BioData mining, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- UDEC-MO: an uncertainty-guided deep embedded clustering framework for bulk and single-cell multi-omics data.Briefings in bioinformatics · 2026Article
- CEDR: robust consensus cancer subtyping with multi-omics data via ensemble dimensionality reduction.Briefings in bioinformatics · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review.Qatar medical journal · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
- Machine learning prediction of germlineBMJ health & care informatics · 2025Article
- DeepCMS: A Feature Selection-Driven Model for Cancer Molecular Subtyping with a Case Study on Testicular Germ Cell Tumors.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- A Review on Biomarker-Enhanced Machine Learning for Early Diagnosis and Outcome Prediction in Ovarian Cancer Management.Cancer medicine · 2025Review
- Identification of a 10-species microbial signature of inflammatory bowel disease by machine learning and external validation.Cell regeneration (London, England) · 2025Article
- MOGAN for LUAD Subtype Classification by Integrating Three Omics Data Types.Cancer innovation · 2025Article
- Deep Clustering-Based Immunotherapy Prediction for Gastric Cancer mRNA Vaccine Development.International journal of molecular sciences · 2025Article
- DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.Briefings in bioinformatics · 2025Article
- A generative deep neural network for pan-digestive tract cancer survival analysis.BioData mining · 2025Article
- Deep learning-based approaches for multi-omics data integration and analysis.BioData mining · 2024Review
- Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database.BioData mining · 2024Article
- A clinical trial termination prediction model based on denoising autoencoder and deep survival regression.Quantitative biology (Beijing, China) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying molecular subtypes of ovarian cancer is important. Compared to identify subtypes using single omics data, the multi-omics data analysis can utilize more information. Autoencoder has been widely used to construct lower dimensional representation for multi-omics feature integration. However, learning in the deep architectures in Autoencoder is difficult for achieving satisfied generalization performance. To solve this problem, we proposed a novel deep learning-based framework to robustly identify ovarian cancer subtypes by using denoising Autoencoder.
resultsIn proposed method, the composite features of multi-omics data in the Cancer Genome Atlas were produced by denoising Autoencoder, and then the generated low-dimensional features were input into
conclusionsThe independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results.
Indexed as
Identifiers
What 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.