CAMELLIA：整合 k-mer 特征选择与监督分类的无比对基因组序列分型流程
CAMELLIA: an alignment-free pipeline integrating k -mer feature selection and supervised classification for genomic sequence subtyping
CAMELLIA 是一个整合特征选择与可解释机器学习的无比对框架，用于基因组序列分析与分类。该方法将序列表示为图并提取结构模式作为特征，可在类别内识别亚组并确保所选特征覆盖代表性不足的亚组。在 HIV-1 案例中，CAMELLIA 将序列准确分为 8 个亚型，处理时间少于 MEGA（MUSCLE）和 FAMSA，在相同数据划分下与 CASTOR-KRFE 持平，准确率优于 COMET 和 REGA。研究还考察了特征数量敏感性，分析了所选特征数与欠拟合或噪声驱动过拟合的关系。
来源：NAR Genomics and Bioinformatics · doi.org
Scalable analysis of biological sequences is essential for functional characterization, biomarker discovery, and therapeutic and vaccine development. Alignment-based pipelines often fail to capture intraspecific variation and become computationally limited on large datasets, while alignment-free methods treat each class as homogeneous, ignoring sample heterogeneity. We present CAMELLIA (Combining AlignMent-free fEature seLection cLassIficAtion), an alignment-free framework that combines feature selection with explainable machine learning for genomic sequence analysis and classification. CAMELLIA identifies subgroups within classes and ensures underrepresented subgroups are captured by the selected features, yielding a more accurate and scalable data-driven representation. Sequences are represented as graphs, from which structural patterns are extracted as features for supervised classifiers, enabling accurate class discrimination. In an HIV-1 case study, CAMELLIA accurately classified sequences into eight subtypes, reduced processing time versus MEGA (MUSCLE) and FAMSA, matched CASTOR-KRFE under identical splits, and outperformed COMET and REGA in accuracy. Beyond predictive performance, CAMELLIA recovered structural patterns beyond class labels, improving feature selection and interpretability. We further examined feature-count sensitivity, showing how selected feature numbers relate to underfitting or noise-driven overfitting. By balancing scalability, accuracy, and interpretability, CAMELLIA offers a new lens for genomic sequence classification and sets a new standard for data-driven, heterogeneity-aware analysis.