Markov chain Monte Carlo for active module identification problem

Markov chain Monte Carlo for active module identification problem

Abstract Background Integrative network methods are commonly used for interpretation of high-throughput experimental biological data: transcriptomics, proteomics, metabolomics and others. One of the common approaches is finding a connected subnetwork of a global interaction network that best encompa...

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Bibliographic Details
Main Authors: Nikita Alexeev, Javlon Isomurodov, Vladimir Sukhov, Gennady Korotkevich, Alexey Sergushichev
Format: Article
Language:English
Published: BMC 2020-11-01
Series:BMC Bioinformatics
Subjects:
Protein-protein interaction
Active module
Markov chain Monte Carlo
Gene expression
Soft classification
Online Access:http://link.springer.com/article/10.1186/s12859-020-03572-9

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http://link.springer.com/article/10.1186/s12859-020-03572-9

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