Protein homology detection using string alignment kernels

Hiroto Saigo, Jean Philippe Vert, Nobuhisa Ueda, Tatsuya Akutsu

研究成果: Contribution to journalArticle査読

280 被引用数 (Scopus)

抄録

Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences. Results: We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection.

本文言語英語
ページ(範囲)1682-1689
ページ数8
ジャーナルBioinformatics
20
11
DOI
出版ステータス出版済み - 7 22 2004
外部発表はい

All Science Journal Classification (ASJC) codes

  • 統計学および確率
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 計算理論と計算数学
  • 計算数学

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