mLS-GKM：基于间隔k-mer支持向量机的高效多类调控序列分类方法
mLS-GKM: Efficient Multi-class Regulatory Sequence Classification with Gapped k-mer SVMs
浏览论文内容
中文总结 AI 辅助
mLS-GKM是LS-GKM的扩展，新增多分类等功能，在322个ENCODE ChIP-seq数据集上速度大幅提升、内存降低，可准确区分调控序列并识别相关基序，提升了gkm-SVMs的可扩展性。
中文摘要 AI 辅助
间隔k-mer支持向量机（gkm-SVMs）被广泛用于调控DNA序列的分类及预测相关序列特征。尽管LS-GKM提供了基于间隔k-mer核的高效实现，但它仅适用于二分类，且无法输出校准后的概率结果。本文提出mLS-GKM，它是LS-GKM的扩展版本，新增了多分类、概率校准预测、并行推理、内存高效的序列解释以及训练过程中的检查点功能。在322个ENCODE ChIP-seq数据集上，使用mLS-GKM训练得到的分类器与LS-GKM生成的分类器性能一致；在64线程下，gkmpredict和gkmexplain的运行速度分别提升了22倍和80倍，且gkmexplain的峰值内存降低了65%以上。实际应用验证显示，mLS-GKM可直接从序列中准确区分增强子、启动子和CTCF结合位点，并识别出具有生物学意义的调控基序。这些改进将gkm-SVMs扩展至多分类问题，大幅提升了其可扩展性，实现了对大型复杂数据集调控DNA序列的高效解释。
英文摘要
Gapped k-mer support vector machines (gkm-SVMs) are widely used for classifying regulatory DNA sequences and identifying the sequence features underlying those predictions. Although LS-GKM provides an efficient implementation of gkm-based kernels, it is restricted to binary classification and does not provide calibrated probability outputs. Here, we present mLS-GKM, an extension of LS-GKM that adds multiclass classification, probability-calibrated predictions, parallelised inference, memory efficient sequence interpretation and checkpointing during training. Across 322 ENCODE ChIP-seq datasets, classifiers trained using mLS-GKM are identical to those produced by LS-GKM, while gkmpredict and gkmexplain run 22x and 80x faster respectively at 64 threads, and gkmexplain peak memory is reduced by more than 65%. As a practical demonstration, mLS-GKM was able to accurately distinguish between enhancers, promoters and CTCF binding sites directly from sequence and identified biologically relevant regulatory motifs. Together, these improvements extend gkm-SVMs to multiclass problems and substantially improve their scalability, enabling efficient interpretation of regulatory DNA sequences in large and complex datasets
