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Discovering time-lagged rules from microarray data using gene profile classifiers.

Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur, Av, Alem 1253, 8000, Bahía Blanca, Argentina.
BMC Bioinformatics (Impact Factor: 3.02). 01/2011; 12:123. DOI: 10.1186/1471-2105-12-123
Source: DBLP

ABSTRACT Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes.
This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations.
A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.

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    ABSTRACT: Gene networks (GNs) have become one of the most important approaches for modelling gene-gene relationships in Bioinformatics (Hecker et al, 2009). These networks allow us to carry out studies of different biological processes in a visual way. Many GN inference algorithms have been developed as techniques for extracting biological knowledge (Ponzoni et al, 2007; Gallo et al, 2011). Once the network has been generated, it is very important to assure network reliability in order to illustrate the quality of the generated model. The quality of a GN can be measured by a direct comparison between the obtained GN and prior biological knowledge (Wei and Li, 2007; Zhou and Wong, 2011). However, these both approaches are not entirely accurate as they only take direct gene–gene interactions into account for the validation task, leaving aside the weak (indirect) relationships (Poyatos, 2011). In this work the authors present a new methodology to assess the biological coherence of a GN. This coherence is obtained according to different biological gene-gene relationships sources. Our proposal is able to perform a complete functional analysis of the input GN. With this aim, graph theory is used to consider not only direct relationships but indirect ones as well.
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Ignacio Ponzoni