SVM-dependent pairwise HMM: an application to protein pairwise alignments | Zendy

Gabriele Orlando | Zendy; Daniele Raimondi | Zendy; Taushif Khan | Zendy; Tom Lenaerts | Zendy; Wim Vranken | Zendy

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SVM-dependent pairwise HMM: an application to protein pairwise alignments

Author(s) -

Gabriele Orlando,

Daniele Raimondi,

Taushif Khan,

Tom Lenaerts,

Wim Vranken

Publication year - 2017

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/btx391

Subject(s) - pairwise comparison , computer science , python (programming language) , benchmark (surveying) , multiple sequence alignment , support vector machine , hidden markov model , sequence alignment , data mining , machine learning , artificial intelligence , biology , peptide sequence , gene , biochemistry , geodesy , geography , operating system

Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions.

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