A SURVEY ON SYNTACTIC PATTERN RECOGNITION METHODS IN BIOINFORMATICS

被引：0
|
作者：
Flasinski, Mariusz [1 ]
机构：
[1] Jagiellonian Univ, Informat Technol Syst Dept, Ul prof St Lojasiewicza 4, PL-30348 Krakow, Poland
来源：
COMPUTER SCIENCE-AGH | 2024年 / 25卷 / 01期
关键词：
bioinformatics; syntactic pattern recognition; generative grammar; hidden Markov model (HMM); HIDDEN MARKOV-MODELS; SECONDARY STRUCTURE PREDICTION; CONTEXT-FREE GRAMMARS; TREE ADJOINING GRAMMARS; GRAPH-GRAMMARS; GRAMMATICAL REPRESENTATIONS; GENOME STRUCTURE; RNA; LANGUAGE; INFERENCE;
D O I：
10.7494/csci.2024.25.1.6153
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Formal tools and models of syntactic pattern recognition which are used in bioinformatics are introduced and characterized in the paper. They include, among others: stochastic (string) grammars and automata, hidden Markov models, programmed grammars, attributed grammars, stochastic tree grammars, Tree Adjoining Grammars (TAGs), algebraic dynamic programming, NLC- and NCE-type graph grammars, and algebraic graph transformation systems. The survey of applications of these formal tools and models in bioinformatics is presented.
引用
收藏
页码：107 / 142
页数：36
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