Modeling dynamic functional relationship networks and application to <i>ex vivo</i> human erythroid differentiation

Zhu, Fan; Shi, Lihong; Li, Hongdong; Eksi, Ridvan; Engel, James Douglas; Guan, Yuanfang

doi:10.1093/bioinformatics/btu542

Cited by 11 publications

(19 citation statements)

References 60 publications

(101 reference statements)

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“…Thereafter, significant efforts have been put into context-specific networks, e.g. processspecific network [68], tissue-specific network based on Bayesian network [79], tissue-specific network based on SVM [80], tissuespecific network based on Gaussian mixture model [81] and developmental stage-specific networks [82,83]. Another popular type of method not discussed in this article is sub-network extraction method [84][85][86][87][88][89], which identifies connected set of genes that significantly differentially overexpressed particular subsets of conditions.…”

Section: Context-specific Network Algorithmsmentioning

confidence: 99%

“…One major limitation of these tissue-specific or global networks is that they are static, whereas one might expect functional relationships to reprogram consistently during differentiation, even within the same tissue [93][94][95]. To address this major limitation, developmental stage-specific network [83,96] and other networks [97,98] have been developed to model such dynamic changes.…”

Section: Developmental Tissue-specific Network With Extra Annotationmentioning

confidence: 99%

“…In the algorithm by Zhu et al 2014 [83], human erythroid cell differentiation is used as an example because of the convenience of isolating a synchronized erythroid cell lineage. As the erythroid differentiation-specific data are collected from one public data set [99,100], a limited number (i.e.…”

Section: Developmental Tissue-specific Network With Extra Annotationmentioning

confidence: 99%

“…Many erythroidrelated genes, which are automatically determined by the stage-specific network, are also supported by existing literature. Figure 3, which is modified from Zhu et al 2014 [83], is an example indicating how developmental stage-specific networks can improve the predicting accuracy on top of global networks. For example, in their comparison, the well-known and confirmed co-functionality relationship of hemoglobin beta (HBB)-hemoglobin alpha 1 and HBB-hemoglobin alpha 2 have a co-functionality probability of only 0.141 and 0.121 in the global network.…”

Section: Developmental Tissue-specific Network With Extra Annotationmentioning

confidence: 99%

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Algorithms for modeling global and context-specific functional relationship networks

Zhu¹,

Panwar²,

Guan³

2015

Brief Bioinform

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Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.

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Section: Context-specific Network Algorithmsmentioning

confidence: 99%

Section: Developmental Tissue-specific Network With Extra Annotationmentioning

confidence: 99%

Section: Developmental Tissue-specific Network With Extra Annotationmentioning

confidence: 99%

Section: Developmental Tissue-specific Network With Extra Annotationmentioning

confidence: 99%

See 2 more Smart Citations

Algorithms for modeling global and context-specific functional relationship networks

Zhu¹,

Panwar²,

Guan³

2015

Brief Bioinform

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“…the regulatory relationships may change across different tissues and diseases. Bioinformatics tools and techniques involving disease-relevant pathways [7, 8], transcriptional co-variance, protein-protein networks [9–12] and phylogenetic conservation [13] have helped to select genes belonging to a certain functional context. With genetic mapping of expression quantitative trait loci (eQTL) studies becoming available for complex renal diseases, these eQTLs will be linked directly to the physical location of transcripts differentially expressed in kidney diseases and support promoter modeling approaches as described in the following example.…”

Section: Integrative Biology Gives Functional Interpretation Of Snps mentioning

confidence: 99%

Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes

Guan

Martini

Mariani

2015

Seminars in Nephrology

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Summary In the past decade, population genetics has gained tremendous success in identifying genetic variations that are statistically relevant to renal diseases and kidney function. However it is challenging to interpret the functional relevance of the genetic variations found by population genetics studies. In this review, we discuss studies that integrate multiple levels of data, especially transcriptome profiles and phenotype data to assign functional roles of genetic variations involved in kidney function. Furthermore, we introduce state-of-the-art machine learning algorithms, Bayesian networks, Support Vector Machines (SVM), and Gaussian Process Regression (GPR), which have been successfully applied to integrating genetic, regulatory, and clinical information to predict clinical outcomes. These methods are likely to be successfully deployed in the nephrology field in the near future.

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Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches

Song

2017

Hidden Markov Models

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In gene expression profile, data analysis pipeline is categorized into four levels, major downstream tasks, i.e., (1) identification of differential expression; (2) clustering co-expression patterns; (3) classification of subtypes of samples; and (4) detection of genetic regulatory networks, are performed posterior to preprocessing procedure such as normalization techniques. To be more specific, temporal dynamic gene expression data has its inherent feature, namely, two neighboring time points (previous and current state) are highly correlated with each other, compared to static expression data which samples are assumed as independent individuals. In this chapter, we demonstrate how HMMs and hierarchical Bayesian modeling methods capture the horizontal time dependency structures in time series expression profiles by focusing on the identification of differential expression. In addition, those differential expression genes and transcript variant isoforms over time detected in core prerequisite steps can be generally further applied in detection of genetic regulatory networks to comprehensively uncover dynamic repertoires in the aspects of system biology as the coupled framework.

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Modeling dynamic functional relationship networks and application to ex vivo human erythroid differentiation

Abstract: The network described in this article is available at http://guanlab.ccmb.med.umich.edu/stageSpecificNetwork.

Cited by 11 publications

References 60 publications

Algorithms for modeling global and context-specific functional relationship networks

Algorithms for modeling global and context-specific functional relationship networks

Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes

Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches

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