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===Techniques inspired by computer science===
[[File:A profile HMM modelling a multiple sequence alignment.png|thumb|A profile HMM modelling a multiple sequence alignment]]
A variety of general [[Optimization (mathematics)|optimization]] algorithms commonly used in computer science have also been applied to the multiple sequence alignment problem. [[Hidden Markov model]]s have been used to produce probability scores for a family of possible multiple sequence alignments for a given query set; although early HMM-based methods produced underwhelming performance, later applications have found them especially effective in detecting remotely related sequences because they are less susceptible to noise created by conservative or semiconservative substitutions.<ref name=karplus>{{cite journal | journal=Bioinformatics | volume=14 | issue=10 | pages= 846–856| year=1998 |author1=Karplus K |author2=Barrett C |author3=Hughey R. | title=Hidden Markov models for detecting remote protein homologies | url=http://bioinformatics.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=9927713 | pmid=9927713 | doi = 10.1093/bioinformatics/14.10.846 | doi-access=free }}</ref> [[Genetic algorithm]]s and [[simulated annealing]] have also been used in optimizing multiple sequence alignment scores as judged by a scoring function like the sum-of-pairs method. More complete details and software packages can be found in the main article [[multiple sequence alignment]].
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