ArticleGenes2018
An Improved Method for Prediction of Cancer Prognosis by Network Learning.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Pan-cancer analysis of the disulfidptosis-related geneHeliyon · 2024Article
- Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer.PLoS computational biology · 2023Article
- Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes.International journal of molecular sciences · 2023Article
- Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring.Biomedicines · 2023Article
- A survival tree based on stabilized score tests for high-dimensional covariates.Journal of applied statistics · 2023Article
- Dynamic Risk Prediction via a Joint Frailty-Copula Model and IPD Meta-Analysis: Building Web Applications.Entropy (Basel, Switzerland) · 2022Article
- Multi-omics data integration by generative adversarial network.Bioinformatics (Oxford, England) · 2021Article
- GVES: machine learning model for identification of prognostic genes with a small dataset.Scientific reports · 2021Article
- Improved cancer biomarkers identification using network-constrained infinite latent feature selection.PloS one · 2021Article
- Article
- Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.Frontiers in oncology · 2020Review
- Generative Adversarial Networks and Its Applications in Biomedical Informatics.Frontiers in public health · 2020Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Accurate identification of prognostic biomarkers is an important yet challenging goal in bioinformatics. Many bioinformatics approaches have been proposed for this purpose, but there is still room for improvement. In this paper, we propose a novel machine learning-based method for more accurate identification of prognostic biomarker genes and use them for prediction of cancer prognosis. The proposed method specifies the candidate prognostic gene module by graph learning using the generative adversarial networks (GANs) model, and scores genes using a PageRank algorithm. We applied the proposed method to multiple-omics data that included copy number, gene expression, DNA methylation, and somatic mutation data for five cancer types. The proposed method showed better prediction accuracy than did existing methods. We identified many prognostic genes and their roles in their biological pathways. We also showed that the genes identified from different omics data were complementary, which led to improved accuracy in prediction using multi-omics data.
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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.