ArticleGenes2022
Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression Using Transfer Learning.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Epigenetics of Malignant Melanoma: Mechanisms, Diagnostic Approaches and Therapeutic Applications.Oncology research · 2026Review
- Article
- Antibacterial Activity of the p53 Tumor Suppressor Protein-How Strong Is the Evidence?International journal of molecular sciences · 2025Review
- Article
- Assessment of Concentration KRT6 Proteins in Tumor and Matching Surgical Margin from Patients with Head and Neck Squamous Cell Carcinoma.International journal of molecular sciences · 2024Article
- Complete chloroplast genomes of three copal trees (Bursera: Bullockia): comparative analysis and phylogenetic relationships.Molecular biology reports · 2024Article
- Current knowledge about immunotherapy resistance for melanoma and potential predictive and prognostic biomarkers.Cancer drug resistance (Alhambra, Calif.) · 2024Review
- Article
- Neurotransmitter receptor-related gene signature as potential prognostic and therapeutic biomarkers in colorectal cancer.Frontiers in cell and developmental biology · 2023Article
- An update on methods for detection of prognostic and predictive biomarkers in melanoma.Frontiers in cell and developmental biology · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set. Further novel biomarkers were also found. Our ensemble model achieved an AUC of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB) in melanoma. The results show the utility of a transfer learning approach for biomarker discovery in melanoma.
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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.