Journal ArticleDOI
Structural Properties of Gene Regulatory Networks: Definitions and Connections
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TLDR
This work formally defines structural properties that are relevant to Gene Regulatory Networks and explains completely the connections between the identifiability conditions and structural criteria of observability and distinguishability.Abstract:
The study of gene regulatory networks is a significant problem in systems biology. Of particular interest is the problem of determining the unknown or hidden higher level regulatory signals by using gene expression data from DNA microarray experiments. Several studies in this area have demonstrated the critical aspect of the network structure in tackling the network modelling problem. Structural analysis of systems has proved useful in a number of contexts, viz., observability, controllability, fault diagnosis, sparse matrix computations etc. In this contribution, we formally define structural properties that are relevant to gene regulatory networks. We explore the structural implications of certain quantitative methods and explain completely the connections between the identifiability conditions and structural criteria of observability and distinguishability. We illustrate these concepts in case studies using representative biologically motivated network examples. The present work bridges the quantitative modelling methods with those based on the structural analysis.read more
Citations
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Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling
TL;DR: Using singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype.
Journal ArticleDOI
A feature selection technique for inference of graphs from their known topological properties: Revealing scale-free gene regulatory networks
TL;DR: A novel methodology that aggregates scale-free properties to a classical low-cost feature selection method, known as Sequential Floating Forward Selection (SFFS), for guiding the inference task and provides smaller estimation errors than those obtained without guiding the SFFS application by the scale- free model, thus maintaining the robustness of the S FFS method.
Journal ArticleDOI
Gene Expression Complex Networks: Synthesis, Identification, and Analysis
TL;DR: The proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.
Journal ArticleDOI
Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets.
Pablo Ivan Pereira Ramos,Luis Willian Pacheco Arge,Nicholas Costa Barroso Lima,Kiyoshi F. Fukutani,Artur T. L. Queiroz +4 more
TL;DR: This review of computational tools that allow for their construction and analysis from high-throughput omics datasets are presented, and emphasis is given to tools’ user-friendliness, including plugins for the widely adopted Cytoscape software.
Journal ArticleDOI
Structural Identifiability in Low-Rank Matrix Factorization
TL;DR: The task is to monitor the signal sources with the cheapest subset of sensors, while maintaining structural identifiability of the model, and it is shown that this problem is NP-hard.
References
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Lethality and centrality in protein networks
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Journal ArticleDOI
Towards a proteome-scale map of the human protein–protein interaction network
Jean François Rual,Kavitha Venkatesan,Tong Hao,Tomoko Hirozane-Kishikawa,Amélie Dricot,Ning Li,Gabriel F. Berriz,Francis D. Gibbons,Matija Dreze,Nono Ayivi-Guedehoussou,Niels Klitgord,Christophe Simon,Mike Boxem,Stuart Milstein,Jennifer Rosenberg,Debra S. Goldberg,Lan V. Zhang,Sharyl L. Wong,Giovanni Franklin,Siming Li,Joanna S. Albala,Joanna S. Albala,Janghoo Lim,Carlene Fraughton,Estelle Llamosas,Sebiha Cevik,Camille Bex,Philippe Lamesch,Robert S. Sikorski,Jean Vandenhaute,Huda Y. Zoghbi,Alex Smolyar,Stephanie Bosak,Reynaldo Sequerra,Lynn Doucette-Stamm,Michael E. Cusick,David E. Hill,Frederick P. Roth,Marc Vidal +38 more
TL;DR: An initial version of a proteome-scale map of human binary protein–protein interactions is described, which increases by ∼70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins.