ArticleThe Journal of investigative dermatology2020
Human against Machine? Machine Learning Identifies MicroRNA Ratios as Biomarkers for Melanoma.
Article in The Journal of investigative dermatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Electrochemical Biosensors for the Detection of Exosomal microRNA Biomarkers for Early Diagnosis of Neurodegenerative Diseases.Analytical chemistry · 2025Review
- Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View.International journal of molecular sciences · 2022Review
- Noncoding RNA as Diagnostic and Prognostic Biomarkers in Cerebrovascular Disease.Oxidative medicine and cellular longevity · 2022Review
- Machine-learning-based Analysis Identifies miRNA Expression Profile for Diagnosis and Prediction of Colorectal Cancer: A Preliminary Study.Cancer genomics & proteomicsArticle
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of quantitative molecular biomarkers to distinguish melanoma from nevi is highly desirable. Expressions of microRNAs (miRNAs) are promising candidates but lack consensus in many studies. Torres et al. (2020) utilized a machine learning pipeline to identify miRNA ratios as strong biomarkers. Results indicate that machine learning, although powerful, requires human input to identify high quality biomarker signatures.
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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.