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

Abstract

BackgroundGene 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.ResultsThis 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.ConclusionsA 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.

Discovering gene association networks by multi-objective evolutionary quantitative association rules

“…We present the performance of GarNet and the comparison of GarNet against other approaches. The comparative study is organized similar to the study achieved in [12], where it was performed in three different stages due to the availability of the methods and the results reported in the literature. First, we discuss in Section 4.2.1 the performance of GarNet using different parameter thresholds to show the robustness of our approach and identify the most influent metrics in the results.…”

“…First, we discuss in Section 4.2.1 the performance of GarNet using different parameter thresholds to show the robustness of our approach and identify the most influent metrics in the results. Afterward, we present in Section 4.2.2 the performance of GarNet versus GRNCOP [11] and GRNCOP2 [12]. We compared average values obtaining from modifying several input parameters.…”

“…Consequently, to compare the inference capability of our approach against other methods we selected as benchmark methods: a decision-tree-based method [9], a regression-tree-based method [3] called RegNet, a probabilistic graphical model [10] and combinatorial optimization algorithms named GRNCOP [11] and GRN-COP2 [12].…”

“…GarNet applies an inference process from K microarray datasets. This process is performed in [12] to evaluate automatically the gene-gene associations obtained by different datasets, therefore improving the degree of evidence required for the potential associations to be returned.…”

“…For performance analysis we applied as benchmark methods a decision-tree-based method [9], a regression-tree based method [3], a probabilistic graphical model [10] and combinatorial optimization algorithm [11,12]. The performance analysis was organized in three steps similarly to the study performed by Gallo et al [12]. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as a true network.…”