References of "Huynh-Thu, Vân Anh"
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See detailGene Regulatory Networks
Sanguinetti, Guido; Huynh-Thu, Vân Anh ULiege

Book published by Humana Press (2019)

This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the ... [more ▼]

This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools. Cutting-edge and thorough, Gene Regulatory Networks: Methods and Protocols is an essential tool for evaluating the current research needed to further address the common challenges faced by specialists in this field. [less ▲]

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See detailTree-based learning of regulatory network topologies and dynamics with Jump3
Huynh-Thu, Vân Anh ULiege; Sanguinetti, Guido

in Sanguinetti, Guido; Huynh-Thu, Vân Anh (Eds.) Gene Regulatory Networks (2019)

Inference of gene regulatory networks (GRNs) from time series data is a well established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and ... [more ▼]

Inference of gene regulatory networks (GRNs) from time series data is a well established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly complementary: model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics, but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability. Since they are not based on any parametric model, they are more flexible that model-based methods, but also less interpretable. In this chapter, we describe Jump3, a hybrid approach that bridges the gap between model-free and model-based methods. Jump3 uses a formal stochastic differential equation to model each gene expression, but reconstructs the GRN topology with a non-parametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step by step tutorial of the associated software usage. [less ▲]

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See detailGene regulatory network inference: An Introductory Survey
Huynh-Thu, Vân Anh ULiege; Sanguinetti, Guido

in Sanguinetti, Guido; Huynh-Thu, Vân Anh (Eds.) Gene Regulatory Networks (2019)

Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, reconstructing the structure of such ... [more ▼]

Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorisation which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialised chapters of this book. [less ▲]

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See detailUnsupervised gene network inference with decision trees and Random forests
Huynh-Thu, Vân Anh ULiege; Geurts, Pierre ULiege

in Sanguinetti, Guido; Huynh-Thu, Vân Anh (Eds.) Gene Regulatory Networks (2019)

In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for ... [more ▼]

In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametric, have good scalability, and have very few parameters. In particular, we describe in detail the GENIE3 algorithm, a state-of-the-art method for GRN inference. [less ▲]

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See detailNets versus trees for feature ranking and gene network inference
Vecoven, Nicolas ULiege; Begon, Jean-Michel ULiege; Huynh-Thu, Vân Anh ULiege et al

E-print/Working paper (2017)

We propose to tackle the challenging problem of gene regulatory network inference, using variable importance measures derived from artifi cial neural networks (ANN). When combined with a L1-regularized ... [more ▼]

We propose to tackle the challenging problem of gene regulatory network inference, using variable importance measures derived from artifi cial neural networks (ANN). When combined with a L1-regularized selection layer, these measures allow ANN to be competitive with state of the art techniques for this problem based on random forests. [less ▲]

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See detailSCENIC: single-cell regulatory network inference and clustering
Aibar, Sara; González-Blas, Carmen Bravo; Moerman, Thomas et al

in Nature Methods (2017), 14

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium ... [more ▼]

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity. [less ▲]

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See detailContext-dependent feature analysis with random forests
Sutera, Antonio ULiege; Louppe, Gilles ULiege; Huynh-Thu, Vân Anh ULiege et al

in Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016) (2016, June)

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See detailStrand-specific, high-resolution mapping of modified RNA polymerase II
Milligan, Laura; Huynh-Thu, Vân Anh ULiege; Delan-Forino, Clémentine et al

in Molecular Systems Biology (2016), 12(6), 874

Reversible modification of the RNAPII C-terminal domain links transcription with RNA processing and surveillance activities. To better understand this, we mapped the location of RNAPII carrying the five ... [more ▼]

Reversible modification of the RNAPII C-terminal domain links transcription with RNA processing and surveillance activities. To better understand this, we mapped the location of RNAPII carrying the five types of CTD phosphorylation on the RNA transcript, providing strand-specific, nucleotide-resolution information, and we used a machine learning-based approach to define RNAPII states. This revealed enrichment of Ser5P, and depletion of Tyr1P, Ser2P, Thr4P, and Ser7P in the transcription start site (TSS) proximal ~150 nt of most genes, with depletion of all modifications close to the poly(A) site. The TSS region also showed elevated RNAPII relative to regions further 3′, with high recruitment of RNA surveillance and termination factors, and correlated with the previously mapped 3′ ends of short, unstable ncRNA transcripts. A hidden Markov model identified distinct modification states associated with initiating, early elongating and later elongating RNAPII. The initiation state was enriched near the TSS of protein-coding genes and persisted throughout exon 1 of intron-containing genes. Notably, unstable ncRNAs apparently failed to transition into the elongation states seen on protein-coding genes. [less ▲]

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See detailCombining tree-based and dynamical systems for the inference of gene regulatory networks
Huynh-Thu, Vân Anh ULiege; Sanguinetti, Guido

Conference (2016)

Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference ... [more ▼]

Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a lack of interpretability, and cannot in general be used for out of sample predictions. On the other hand, model-based methods focus on identifying a dynamical model of the system; these are clearly interpretable and can be used for predictions, however they rely on strong assumptions and are typically very demanding computationally. Here, we propose a new hybrid approach for GRN inference, called Jump3, exploiting time series of expression data. Jump3 is based on a formal on/off model of gene expression, but uses a non-parametric procedure based on decision trees (called “jump trees”) to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes. We show the good performance of Jump3 on in silico and synthetic networks, and applied the approach to identify regulatory interactions activated in the presence of interferon gamma. Our MATLAB implementation of Jump3 is available at http://www.montefiore.ulg.ac.be/~huynh-thu/software.html. [less ▲]

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See detailCombining tree-based and dynamical systems for the inference of gene regulatory networks
Huynh-Thu, Vân Anh ULiege; Sanguinetti, Guido

Conference (2015, December 07)

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See detailCombining tree-based and dynamical systems for the inference of gene regulatory networks
Huynh-Thu, Vân Anh ULiege; Sanguinetti, Guido

Poster (2015, April)

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See detailCombining tree-based and dynamical systems for the inference of gene regulatory networks
Huynh-Thu, Vân Anh ULiege; Sanguinetti, Guido

in Bioinformatics (2015), 31(10), 1614-1622

Motivation: Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN ... [more ▼]

Motivation: Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a lack of interpretability and cannot in general be used for out of sample predictions. On the other hand, model-based methods focus on identifying a dynamical model of the system. These are clearly interpretable and can be used for predictions; however, they rely on strong assumptions and are typically very demanding computationally. Results: Here, we propose a new hybrid approach for GRN inference, called Jump3, exploiting time series of expression data. Jump3 is based on a formal on/off model of gene expression but uses a non-parametric procedure based on decision trees (called "jump trees") to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes. We show the good performance of Jump3 on in silico and synthetic networks and applied the approach to identify regulatory interactions activated in the presence of interferon gamma. Availability and implementation: Our MATLAB implementation of Jump3 is available at http:// homepages.inf.ed.ac.uk/vhuynht/software.html. [less ▲]

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See detailMapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference
Potier, Delphine; Davie, Kristofer; Hulselmans, Gert et al

in Cell Reports (2014), 9(6), 2290-2303

Genome control is operated by transcription factors (TFs) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their ... [more ▼]

Genome control is operated by transcription factors (TFs) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRNs). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse engineer the GRNs of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types and inferred a coexpression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5,632 direct target genes through 24,926 enhancers. Using this network, we found network motifs, cis-regulatory codes, and regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general cofactor in the eye network, being bound to thousands of nucleosome-free regions. [less ▲]

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See detailA hybrid approach for the inference and modelling of gene regulatory networks
Huynh-Thu, Vân Anh ULiege; Sanguinetti, Guido

Conference (2014, September 06)

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See detailNIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms
Ruyssinck, Joeri; Huynh-Thu, Vân Anh ULiege; Geurts, Pierre ULiege et al

in PLoS ONE (2014), 9(3), 92709

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts ... [more ▼]

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. [less ▲]

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See detailBridging physiological and evolutionary time-scales in a gene regulatory network.
Marchand, Gwenaelle; Huynh-Thu, Vân Anh ULiege; Kane, Nolan C. et al

in New Phytologist (2014), 203(2), 685-696

Gene regulatory networks (GRNs) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time-scales. To identify patterns ... [more ▼]

Gene regulatory networks (GRNs) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time-scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution. We examined the responses of 32 423 expressed sequences to drought and to abscisic acid (ABA) and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian graphical model and a random forest algorithm and studied the genetic differentiation among populations (FST ) at nodes. We identified two main hubs in the network that transport nitrate in guard cells. This suggests that nitrate transport is a critical aspect of the sunflower physiological response to drought. We observed that differentiation of the network genes in elite sunflower cultivars is correlated with their position and connectivity. This systems biology approach combined molecular data at different time-scales and identified important physiological processes. At the evolutionary level, we propose that network topology could influence responses to human selection and possibly adaptation to dry environments. [less ▲]

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See detailIdentification of a microRNA landscape targeting the PI3K/Akt signaling pathway in inflammation-induced colorectal carcinogenesis
JOSSE, Claire ULiege; Bouznad, Nassim ULiege; Geurts, Pierre ULiege et al

in American Journal of Physiology - Gastrointestinal and Liver Physiology (2014), 306

Inflammation can contribute to tumor formation; however, markers that predict progression are still lacking. In the present study, the well-established azoxymethane (AOM)/dextran sulfate sodium (DSS ... [more ▼]

Inflammation can contribute to tumor formation; however, markers that predict progression are still lacking. In the present study, the well-established azoxymethane (AOM)/dextran sulfate sodium (DSS)-induced mouse model of colitis-associated cancer was used to analyze microRNA (miRNA) modulation accompanying inflammation-induced tumor development and to determine whether inflammation-triggered miRNA alterations affect the expression of genes or pathways involved in cancer. A miRNA microarray experiment was performed to establish miRNA expression profiles in mouse colon at early and late time points during inflammation and/or tumor growth. Chronic inflammation and carcinogenesis were associated with distinct changes in miRNA expression. Nevertheless, prediction algorithms of miRNA-mRNA interactions and computational analyses based on ranked miRNA lists consistently identified putative target genes that play essential roles in tumor growth or that belong to key carcinogenesis-related signaling pathways. We identified PI3K/Akt and the insulin growth factor-1 (IGF-1) as major pathways being affected in the AOM/DSS model. DSS-induced chronic inflammation downregulates miR-133a and miR-143/145, which is reportedly associated with human colorectal cancer and PI3K/Akt activation. Accordingly, conditioned medium from inflammatory cells decreases the expression of these miRNA in colorectal adenocarcinoma Caco-2 cells. Overexpression of miR-223, one of the main miRNA showing strong upregulation during AOM/DSS tumor growth, inhibited Akt phosphorylation and IGF-1R expression in these cells. Cell sorting from mouse colons delineated distinct miRNA expression patterns in epithelial and myeloid cells during the periods preceding and spanning tumor growth. Hence, cell-type-specific miRNA dysregulation and subsequent PI3K/Akt activation may be involved in the transition from intestinal inflammation to cancer. [less ▲]

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See detailNetwork reconstruction from time series expression data using tree-based methods
Huynh-Thu, Vân Anh ULiege

Conference (2013, June 26)

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See detailGene regulatory network inference from systems genetics data using tree-based methods
Huynh-Thu, Vân Anh ULiege; Wehenkel, Louis ULiege; Geurts, Pierre ULiege

in de la Fuente, Alberto (Ed.) Gene Network Inference - Verification of Methods for Systems Genetics Data (2013)

One of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics ... [more ▼]

One of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics is to exploit the natural variations that exist between the DNA sequences of related individuals and that can represent the randomized and multifactorial perturbations necessary to recover GRNs. In this chapter, we present new methods, called GENIE3-SG-joint and GENIE3- SG-sep, for the inference of GRNs from systems genetics data. Experiments on the artificial data of the StatSeq benchmark and of the DREAM5 Systems Genetics challenge show that exploiting jointly expression and genetic data is very helpful for recovering GRNs, and one of our methods outperforms by a large extent the official best performing method of the DREAM5 challenge. [less ▲]

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