ArticleBiomolecules2020
Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses 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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- Research Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials.ACS omega · 2026Review
- Deciphering RNA and protein expression discordance identifies TOP2A as a prognostic biomarker and potential therapeutic target in lung adenocarcinoma.Discover oncology · 2026Article
- Identification of Clinically Relevant Genes Using Machine Learning Techniques.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A machine learning approach for multimodal data fusion for survival prediction in cancer patients.NPJ precision oncology · 2025Article
- The application of organoids in investigating immune evasion in the microenvironment of gastric cancer and screening novel drug candidates.Molecular cancer · 2025Review
- Current status and future direction of cancer research using artificial intelligence for clinical application.Cancer science · 2025Review
- High-precision lung cancer subtype diagnosis on imbalanced exosomal data via Exo-LCClassifier.Frontiers in genetics · 2025Article
- Harnessing the power of artificial intelligence for human living organoid research.Bioactive materials · 2024Review
- Emerging trends and hotspots in lung cancer-prediction models research.Annals of medicine and surgery (2012) · 2024Review
- Introduction of AI Technology for Objective Physical Function Assessment.Bioengineering (Basel, Switzerland) · 2024Review
- A model-free and distribution-free multi-omics integration approach for detecting novel lung adenocarcinoma genes.Scientific reports · 2024Article
- IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.Briefings in bioinformatics · 2024Article
- Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis.NPJ precision oncology · 2024Article
- AI-enabled organoids: Construction, analysis, and application.Bioactive materials · 2024Review
- Autoencoder-based multimodal prediction of non-small cell lung cancer survival.Scientific reports · 2023Article
- Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers.Biomolecules · 2023Article
- Identification of gene profiles related to the development of oral cancer using a deep learning technique.BMC medical genomics · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 1 country.
Funding
Abstract
Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test:
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.