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Probabilistic suffix array: efficient modeling and prediction of protein families
Author(s) -
Jie Lin,
Donald Adjeroh,
BingHua Jiang
Publication year - 2012
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/bts121
Subject(s) - markov chain , suffix , probabilistic logic , suffix tree , markov model , hidden markov model , compressed suffix array , tree (set theory) , computer science , algorithm , generalized suffix tree , statistical model , mathematics , theoretical computer science , combinatorics , data structure , artificial intelligence , machine learning , philosophy , linguistics , programming language
Markov models are very popular for analyzing complex sequences such as protein sequences, whose sources are unknown, or whose underlying statistical characteristics are not well understood. A major problem is the computational complexity involved with using Markov models, especially the exponential growth of their size with the order of the model. The probabilistic suffix tree (PST) and its improved variant sparse probabilistic suffix tree (SPST) have been proposed to address some of the key problems with Markov models. The use of the suffix tree, however, implies that the space requirement for the PST/SPST could still be high.

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