【Machine Learning Classification of Prostate Cancer Genomic Sequences Using K-Mer and Sequence-Derived Features】
#论文发表# #前列腺癌# #机器学习# #基因组序列#

Current diagnostic approaches for prostate cancer, including prostate-specific antigen testing and biopsy, lack sufficient specificity and sensitivity, underscoring the need for accurate, molecular-level classification tools. Advances in computational genomics and machine learning (ML) offer a promising path toward sequence-based, objective PCa classification. This study presents a rigorous machine learning framework for binary classification of prostate cancer genomic sequences, integrating k-mer frequency analysis, physicochemical sequence descriptors, SMOTE-based class balancing, and feature importance.

DOI: 10.4236/cmb.2026.162002
http://t.cn/AXovWUy8