Gapped k-mer kernels with support vector machines (gkm-SVMs) have achieved strong predictive performance on regulatory DNA sequences on modestly sized training sets. However, existing gkm-SVM algorithms suffer from slow kernel computation time, as they depend exponentially on the sub-sequence feature length, number of mismatch positions, and the task’s alphabet size.

中文翻译：

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FastSK：使用空缺的字符串内核进行快速序列分析

空位ķ -mer内核的支持向量机（GKM-SVM）的对中等规模的训练集所取得的调节DNA序列较强的预测性能。但是，现有的gkm-SVM算法的内核计算时间很慢，因为它们指数取决于子序列特征的长度，不匹配位置的数量以及任务的字母大小。