ArticleScientific reports2025
Refining cancer prediction with DNA sequencing and combined machine learning approaches.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
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1 author.
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Abstract
A high-accuracy DNA-based cancer risk predictor was developed by blending Logistic Regression with Gaussian Naive Bayes, and its hyperparameters were optimized via grid search. Five cancer types (BRCA1, KIRC, COAD, LUAD, PRAD) were classified in a cohort of 390 patients. Accuracies of 100% were attained for BRCA1, KIRC, and COAD, while 98% was achieved for LUAD and PRAD-representing improvements of 1-2% over recent deep-learning and multi-omic benchmarks. A micro- and macro-average ROC AUC of 0.99 was obtained. The blended ensemble was shown to outperform each individual algorithm and surpass existing state-of-the-art methods, providing a lightweight, interpretable, and highly effective tool for early cancer prediction.
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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.