FastSK: fast sequence analysis with gapped string kernels | Zendy

Derrick Blakely | Zendy; Eamon Collins | Zendy; Ritambhara Singh | Zendy; A. J. Norton | Zendy; Jack Lanchantin | Zendy; Yanjun Qi | Zendy

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FastSK: fast sequence analysis with gapped string kernels

Author(s) -

Derrick Blakely,

Eamon Collins,

Ritambhara Singh,

A. J. Norton,

Jack Lanchantin,

Yanjun Qi

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa817

Subject(s) - computer science , string kernel , algorithm , kernel (algebra) , support vector machine , scalability , computation , source code , classifier (uml) , artificial intelligence , kernel method , mathematics , polynomial kernel , combinatorics , database , operating system

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.

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