ArticleDiagnostics (Basel, Switzerland)2019
A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer.
Article in Diagnostics (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 40 citations in OpenAlex.
- THe Biom: a platform for visualization and exploration of cancer transcriptomic biomarkers identified by robust feature selection.Bioinformatics advances · 2026Article
- Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.PloS one · 2026Article
- Development and validation of an interpretable machine learning model for predicting Gleason score upgrade in prostate cancer.Translational andrology and urology · 2025Article
- Telomere-related gene risk model predicts prognostic and immune microenvironment alterations in prostate cancer.Scientific reports · 2025Article
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- Machine learning discrimination of Gleason scores below GG3 and above GG4 for HSPC patients diagnosis.Scientific reports · 2024Article
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- Evaluating Inflammatory Bowel Disease-Related Quality of Life Using an Interpretable Machine Learning Approach: A Multicenter Study in China.Journal of inflammation research · 2024Article
- The Olfaction Ability of Medical Detection Canine to Detect Prostate Cancer From Urine Samples: Progress Captured in Systematic Review and Meta-Analysis.World journal of oncology · 2023Article
- LncRNA LNC-565686 Promotes Proliferation of Prostate Cancer by Inhibiting Apoptosis through Stabilizing SND1.Biomedicines · 2023Article
- Dysbindin Domain-Containing 1 in Prostate Cancer: New Insights into Bioinformatic Validation of Molecular and Immunological Features.International journal of molecular sciences · 2023Article
- Follow-Up Biomarkers in the Evolution of Prostate Cancer, Levels ofInternational journal of molecular sciences · 2022Article
- Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis.Translational cancer research · 2022Article
- On an Affordable Approach towards the Diagnosis and Care for Prostate Cancer Patients Using Urine, FTIR and Prediction Machines.Diagnostics (Basel, Switzerland) · 2022Article
- Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks.Bioinformatics (Oxford, England) · 2022Article
- One-Day Prostate Cancer Diagnosis: Biparametric Magnetic Resonance Imaging and Digital Pathology by Fluorescence Confocal Microscopy.Diagnostics (Basel, Switzerland) · 2022Article
- Multi-omics analysis of expression and prognostic value of NSUN members in prostate cancer.Frontiers in oncology · 2022Article
- Validating METCAM/MUC18 as a Novel Biomarker to Predict the Malignant Potential of Prostate Cancer at an Early Stage by Using a Modified Gold Nanoparticles-Based Lateral Flow Immunoassay.Diagnostics (Basel, Switzerland) · 2021Article
- Article
- An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
(1) Background:One of the most common cancers that affect North American men and men worldwide is prostate cancer. The Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. Advancements in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason scores more accurately and effectively. (2) Methods: In this study, we used a novel machine learning method to analyse gene expression of prostate tumours with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository, and categorised patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as
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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.