Elastic-Net Copula Granger Causality for Inference of Biological Networks

Furqan, Mohammad Shaheryar; Siyal, Mohammed Yakoob

doi:10.1371/journal.pone.0165612

Cited by 11 publications

(13 citation statements)

References 54 publications

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“…λ 1 is the l 1 -norm regularization parameter, and λ 2 is the regularization parameter for the squared l 2 -norm. Despite elastic net has one more parameter (l 2 -norm) than lasso, it greatly effects the calculation result and works well in solving the grouping effect (Furqan and Siyal, 2016 ). Because l 2 -norm (Hoerl and Kennard, 2000 ) performs well with many variables that are highly correlated and can effectively adjust the high correlation between independent variables so that the model can automatically choose related features in a group with grouping effect (Friedman et al, 2010b ).…”

Section: Methodsmentioning

confidence: 99%

“…To solve the problem of the grouping effect among brain regions, we propose two alternative methods to improve the construction of a hyper-network: (1) the elastic net (De Mol et al, 2008 ; Furqan and Siyal, 2016 ; Teipel et al, 2017 ) and (2) the group lasso method (Friedman et al, 2010a ; Yu et al, 2015 ; Souly and Shah, 2016 ). Then we extracted features using the different clustering coefficients defined by hyper-network to depict the functional brain network topology and performed non-parametric test to select those features with significant difference.…”

Section: Introductionmentioning

confidence: 99%

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Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods

Guo

et al. 2018

Front. Neuroinform.

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Brain network analysis has been widely applied in neuroimaging studies. A hyper-network construction method was previously proposed to characterize the high-order relationships among multiple brain regions, where every edge is connected to more than two brain regions and can be represented by a hyper-graph. A brain functional hyper-network is constructed by a sparse linear regression model using resting-state functional magnetic resonance imaging (fMRI) time series, which in previous studies has been solved by the lasso method. Despite its successful application in many studies, the lasso method has some limitations, including an inability to explain the grouping effect. That is, using the lasso method may cause relevant brain regions be missed in selecting related regions. Ideally, a hyper-edge construction method should be able to select interacting brain regions as accurately as possible. To solve this problem, we took into account the grouping effect among brain regions and proposed two methods to improve the construction of the hyper-network: the elastic net and the group lasso. The three methods were applied to the construction of functional hyper-networks in depressed patients and normal controls. The results showed structural differences among the hyper-networks constructed by the three methods. The hyper-network structure obtained by the lasso was similar to that obtained by the elastic net method but very different from that obtained by the group lasso. The classification results indicated that the elastic net method achieved better classification results than the lasso method with the two proposed methods of hyper-network construction. The elastic net method can effectively solve the grouping effect and achieve better classification performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods

Guo

et al. 2018

Front. Neuroinform.

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“…For examining the potential causal relationships and network structure, autoregressive models for gene regulatory network inference using time course data for sparsity, stability and causality were investigated [ 117 ]. Granger causality approach have been developed for genetic network constructions, and applied for measuring the predictive causality of temporal data [ 57 , 114 , 118 – 122 ]. Furqan and Siyal proposed the LASSO-based Elastic-Net Copula Granger causality for biological network modeling [ 118 ].…”

Section: Introductionmentioning

confidence: 99%

“…Granger causality approach have been developed for genetic network constructions, and applied for measuring the predictive causality of temporal data [ 57 , 114 , 118 – 122 ]. Furqan and Siyal proposed the LASSO-based Elastic-Net Copula Granger causality for biological network modeling [ 118 ]. Their proposed method shows the merits of overcoming high dimensionality issues of ordinary least-squares methods and linear constraints.…”

Section: Introductionmentioning

confidence: 99%

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Computational dynamic approaches for temporal omics data with applications to systems medicine

Liang

Kelemen

2017

BioData Mining

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Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

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Inference of gene regulatory networks using pseudo-time series data

Zhang

Chang

2021

Bioinformatics

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Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific data set. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Results Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. Availability The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary information Supplementary data are available at Bioinformatics online.

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Elastic-Net Copula Granger Causality for Inference of Biological Networks

Cited by 11 publications

References 54 publications

Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods

Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods

Computational dynamic approaches for temporal omics data with applications to systems medicine

Inference of gene regulatory networks using pseudo-time series data

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