ArticleNPJ precision oncology2024
Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review.Cancer reports (Hoboken, N.J.) · 2025Pooled it
- Deep Generative and Graph-Based Representation Learning for Multiomics Survival Stratification in Ovarian Cancer: Secondary Analysis.JMIR bioinformatics and biotechnology · 2026Article
- BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women.Briefings in bioinformatics · 2026Article
- [Paeoniflorin alleviates cancer-related fatigue during chemotherapy for breast cancer by targeting EZH2/CCNE1 to regulate cell cycle and inflammatory microenvironment].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- MoESurv: a zero-sample and transferable survival prediction framework for rare cancers using mixture of experts.Bioinformatics (Oxford, England) · 2026Article
- Sparsity is all you need: rethinking biologically informed neural networks.Briefings in bioinformatics · 2026Article
- Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.Journal of ovarian research · 2026Article
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- Artificial intelligence and multi-omics integration in liquid biopsy for genitourinary cancers: a systematic scoping review.International urology and nephrology · 2026Article
- Predicting Postoperative Survival in Patients With Malignant Biliary Obstruction Using an Interpretable Machine Learning Model: A Multicenter Study.Cancer medicine · 2026Article
- Transforming multi-omics data into images for disease classification: A review of techniques and tools.Journal of pathology informatics · 2026Review
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Transcriptome Transformer: improving patient survival prediction via multitask learning of transcriptomic and clinical features.Briefings in bioinformatics · 2025Article
- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- Cancer survival prediction based on soft-label guided contrastive learning and global feature fusion.Bioinformatics (Oxford, England) · 2025Article
- Predicting postoperative fever in culture-negative patients undergoing mini-PCNL using MAP score-augmented machine learning: a retrospective cohort study.World journal of urology · 2025Article
- Medical laboratory data-based models: opportunities, obstacles, and solutions.Journal of translational medicine · 2025Review
- Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics.Briefings in bioinformatics · 2025Review
- Article
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Authors and funding
7 authors.
Funding
Abstract
Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies.
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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.