Recent methods for rna modeling using stochastic context-free grammars

Yasubumi Sakakibara, Michael Brown, Richard Hughey, Saira Mian, Kimmen Sjölander, Rebecca C. Underwood, David Haussler

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

Stochastic context-free grammars (SC, FGs) Call be applied to the problems of folding, aligning and modeling families of homologous RNA sequences. SCFGs capture tile sequences’ common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. This paper discusses our new algorithm, Tree-Grammar EM, for deducing SCFG parameters automatically from unaligned, unfolded training sequences. Tree-Grammar EM, a generalization of tile HMM forward-backward algorithm, is based on tree grammars and is faster than tile previously proposed inside-outside SCFG training algorithm. Independently, Scan Eddy and Richard Durbin have introduced a trainable “covariance model” (CM) to perform similar tasks. We compare and contrast our methods with theirs.

本文言語English
ホスト出版物のタイトルCombinatorial Pattern Matching - 5th Annual Symposium, CPM 1994, Proceedings
編集者Maxime Crochemore, Dan Gusfield
出版社Springer Verlag
ページ290-306
ページ数17
ISBN(印刷版)9783540580942
DOI
出版ステータスPublished - 1994 1 1
外部発表はい
イベント5th Annual Symposium on Combinatorial Pattern Matching, CPM 1994 - Asilomar, United States
継続期間: 1994 6 51994 6 8

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
807 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other5th Annual Symposium on Combinatorial Pattern Matching, CPM 1994
CountryUnited States
CityAsilomar
Period94/6/594/6/8

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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