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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Research Article

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

Author(s): Karel Diéguez-Santana, Gerardo M. Casañola-Martin, James R. Green, Bakhtiyor Rasulev and Humberto González-Díaz*

Volume 21, Issue 9, 2021

Published on: 31 March, 2021

Page: [819 - 827] Pages: 9

DOI: 10.2174/1568026621666210331161144

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Abstract

Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology.

Objective: In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions).

Results: The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series.

Methods: In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis’ group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms.

Conclusion: Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.

Keywords: Metabolic pathways, Complex networks, Combinatorial perturbation theory models, Machine learning, linear invariants, Markov chains.

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