A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
Accepted version
Peer-reviewed
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Authors
Bardozzo, Francesco
Lio', Pietro
Tagliaferri, Roberto
Abstract
In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . This is a consequence of the alternation of the omic values of codon usage and protein abundance along with the circuits. In this work, the E.Coli's Glycolysis and its multi-omic circuit features are shown as an example.
Description
Keywords
q-bio.MN, q-bio.MN, cs.LG, stat.ML
Journal Title
CoRR
Conference Name
13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
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Volume Title
2016
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All rights reserved