Gene Regulatory Network Inference from Systems Genetics Data Using Tree-Based Methods

Huynh-Thu, Vân Anh; Wehenkel, Louis; Geurts, Pierre

doi:10.1007/978-3-642-45161-4_5

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…Another variation on this theme is GENIE3, which was also used in the DREAM5 challenge, and uses random forest regression to make the method more flexible and non-parametric. This method was first presented in [30], and then improved in [31] and [32].…”

Section: Basic Statistical Methodsmentioning

confidence: 99%

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

Bernaola-Alvarez

Michiels

Larrañaga

et al. 2020

Preprint

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We present FGES-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competitive with the state of the art in terms of the recall of the true structure while also improving upon it in terms of speed, scaling up to the tens of thousands of variables and being able to use empirical knowledge about the topological structure of gene regulatory networks. We apply this method to learning the gene regulatory network for the full human genome using data from samples of different brain structures (from the Allen Human Brain Atlas). Our goal is to develop a Bayesian network model that predicts interactions between genes in a way that is clear to experts, following the current trends in interpretable artificial intelligence. To achieve this, we also present a new open-access visualization tool that facilitates the exploration of massive networks and can aid in finding nodes of interest for experimental tests.

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Section: Basic Statistical Methodsmentioning

confidence: 99%

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

Bernaola-Alvarez

Michiels

Larrañaga

et al. 2020

Preprint

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“…Random Forests can perform non-linear predictions and, thus, often outperform linear models. Since its introduction by Breiman (2001), Random Forests have been widely used in many fields from gene regulatory network inference to generic image classification (Huynh-Thu et al, 2013; Marée et al, 2016). Random Forest relies on growing a multitude of decision trees, a prediction algorithm that has shown good performances by itself but, when combined with other decision trees (hence the name forest), returns predictions that are much more robust to outliers and noisy data (see bootstrap aggregating, Breiman, 1996).…”

Section: Methodsmentioning

confidence: 99%

Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines

et al. 2017

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Root system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases. We have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000). For each image, three levels of noise were created. This library was used to evaluate the accuracy and usefulness of several image descriptors classically used in root image analysis softwares. Our analysis highlighted that the accuracy of the different traits is strongly dependent on the quality of the images and the type, size, and complexity of the root systems analyzed. Our study also demonstrated that machine learning algorithms can be trained on a synthetic library to improve the estimation of several root system traits. Overall, our analysis is a call to caution when using automatic root image analysis tools. If a thorough calibration is not performed on the dataset of interest, unexpected errors might arise, especially for large and complex root images. To facilitate such calibration, both the image library and the different codes used in the study have been made available to the community.

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“…We next employed an unbiased gene regulatory network (GRN) inference approach to decipher the interactions of 96 experimentally validated and manually curated transcription factors (33) in regulating these 15 clusters using GENIE3 (34). We illustrate the top eight transcription factors that have multiple regulatory connections to the sex clusters (Fig.…”

Section: Inferring Interactions Of Transcription Factors With Sexual ...mentioning

confidence: 99%

A single cell atlas of sexual development in Plasmodium falciparum

Dogga,

Rop,

Cudini

et al. 2024

Science

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The developmental decision made by malaria parasites to become sexual underlies all malaria transmission. Here, we describe a rich atlas of short- and long-read single-cell transcriptomes of over 37,000 Plasmodium falciparum cells across intraerythrocytic asexual and sexual development. We used the atlas to explore transcriptional modules and exon usage along sexual development and expanded it to include malaria parasites collected from four Malian individuals naturally infected with multiple P. falciparum strains. We investigated genotypic and transcriptional heterogeneity within and among these wild strains at the single-cell level, finding differential expression between different strains even within the same host. These data are a key addition to the Malaria Cell Atlas interactive data resource, enabling a deeper understanding of the biology and diversity of transmission stages.

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Gene Regulatory Network Inference from Systems Genetics Data Using Tree-Based Methods

Cited by 7 publications

References 37 publications

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines

A single cell atlas of sexual development in Plasmodium falciparum

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