Supervised Classification of Metabolic Networks

Granata I.; Guarracino M.; V. A. Kalyagin; Maddalena L.; Manipur I.; P. M. Pardalos

doi:10.1109/BIBM.2018.8621500

Publications

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Supervised Classification of Metabolic Networks

P. 2688–2693.

Granata I., Guarracino M., Kalyagin V. A., Maddalena L., Manipur I., Pardalos P. M.

Networks represent a convenient model for many scientific and technological problems. From power grids to biological processes and functions, from financial networks to chemical compounds, the representation of case studies with graphs enables the possibility to highlight both topological and qualitative characteristics. In this work, we are interested in the supervised classification models for data in form of networks. Given two or more classes whose members are networks, we want to build a mathematical model to classify them. We focus on networks with labeled nodes and weighted edges. We define distances between networks and we build a classification model. We provide empirical results on datasets of biological interest providing details on graphical model selection.

Keywords: Supervised classification Network data Metabolic networks

Publication based on the results of:

Methods and Algorithms for Networks Analysis (2019)

In book

Proceedings 2018 IEEE International Conference on Bioinformatics and Biomedicine

Madrid: IEEE, 2018.

Evolution of transcriptional regulation of histidine metabolism in Gram-positive bacteria

German A. Ashniev, Sernova N., Shevkoplias A. et al., BMC Genomics 2022 Vol. 23 Article 558

The histidine metabolism and transport (his) genes are controlled by a variety of RNA-dependent regulatory systems among diverse taxonomic groups of bacteria including T-box riboswitches in Firmicutes and Actinobacteria and RNA attenuators in Proteobacteria. Using a comparative genomic approach, we previously identified a novel DNA-binding transcription factor (named HisR) that controls the histidine metabolism genes ...

Added: January 31, 2023

Learning from Metabolic Networks: Current Trends and Future Directions for Precision Medicine

Granata I., Manzo M., Kusumastuti A. et al., Current Medicinal Chemistry 2021 Vol. 28 No. 32 P. 6619–6653

Purpose: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a ...

Added: August 25, 2021

Clustering analysis of tumor metabolic networks

Manipur I., Granata I., Maddalena L. et al., BMC Bioinformatics 2020 Vol. 21 No. 10 P. 349

Background: Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is ...

Added: November 22, 2020

Model simplification for supervised classification of metabolic networks

Granata I., Guarracino M., Kalyagin V. A. et al., Annals of Mathematics and Artificial Intelligence 2020 Vol. 88 P. 91–104

Many real applications require the representation of complex entities and their relations. Frequently, networks are the chosen data structures, due to their ability to highlight topological and qualitative characteristics. In this work, we are interested in supervised classification models for data in the form of networks. Given two or more classes whose members are networks, ...

Added: November 24, 2019

Statistical testing of segment homogeneity in classification of piecewise-regular objects

Savchenko A., Belova N. S., International Journal of Applied Mathematics and Computer Science 2015 Vol. 25 No. 4 P. 915–925

The paper is focused on the problem of multi-class classification of composite (piecewise-regular) objects (e.g., speech signals, complex images, etc.). We propose a mathematical model of composite object representation as a sequence of independent segments. Each segment is represented as a random sample of independent identically distributed feature vectors. Based on this model and statistical ...

Added: September 10, 2015

Cox process functional learning

Biau G., Cadre B., Paris Q., Statistical Inference for Stochastic Processes 2015 Vol. 18 No. 3 P. 257–277

This article addresses the problem of functional supervised classification of Cox process trajectories, whose random intensity is driven by some exogenous random covariable. The classification task is achieved through a regularized convex empirical risk minimization procedure, and a non asymptotic oracle inequality is derived. We show that the algorithm provides a Bayes-risk consistent classifier. Furthermore, ...

Added: April 28, 2015