ArticleInternational journal of molecular sciences2025
101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Molecular mechanisms, current therapeutics, and emerging strategies in esophageal cancer: A comprehensive review.Chinese medical journal · 2026Review
- Integrative bioinformatics and experiments identify RIBC2 as a key regulator in the esophageal cancer.PloS one · 2026Article
- Development and external validation of a machine learning-based prognostic model for small cell neuroendocrine cervical carcinoma: a multi-center study.BMC cancer · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the digestive tract, characterized by a high recurrence rate and inadequate immunotherapy options. We analyzed mutation data of ESCC from public databases and employed 10 machine learning algorithms to generate 101 algorithm combinations. Based on the optimal range determined by the concordance index, we randomly selected one combination from the best-performing algorithms to construct a prognostic model consisting of five genes (
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