Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer

• Published in: BMC bioinformatics Vol. 24; no. 1; p. 465
• Main Authors: Yang, Youpeng, Zeng, Qiuhong, Liu, Gaotong, Zheng, Shiyao, Luo, Tianyang, Guo, Yibin, Tang, Jia, Huang, Yi
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.12.2023; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Brain tumors; Cancer; Classification; Classifiers; Cluster analysis; Clustering; Datasets; Development and progression; Diagnosis; DNA Methylation; Epigenome; Evaluation; Gene expression; Genetic aspects; Genetic testing; Genomes; Genomics; Humans; Machine learning; Medulloblastoma; Methylation; Mutation; Neoplasms - diagnosis; Neoplasms - genetics; Pituitary; Predictions; Sarcoma; Tumors; Variance analysis; China; Cluster analysis; Classification; Machine learning; Cancer

Hierarchical classification offers a more specific categorization of data and breaks down large classification problems into subproblems, providing improved prediction accuracy and predictive power for undefined categories, while also mitigating the impact of poor-quality data. Despite these advanta...