scholarly journals Inference of plant gene regulatory networks using data-driven methods: A practical overview

2020 ◽  
Vol 1863 (6) ◽  
pp. 194447 ◽  
Author(s):  
Shubhada R. Kulkarni ◽  
Klaas Vandepoele
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Data Driven ◽  
Plant Gene ◽  
Gene Regulatory ◽  
Using Data

Inference and analysis of gene regulatory networks using data mining techniques

2020 ◽  
Vol 10 (1) ◽  
pp. 1-2
Author(s):  
Syed Sazzad Ahmed ◽  
Swarup Roy
Keyword(s):  
Data Mining ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Data Mining Techniques ◽  
Gene Regulatory ◽  
Using Data

scholarly journals D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S13) ◽  
Author(s):  
Xiang Chen ◽  
Min Li ◽  
Ruiqing Zheng ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Dynamic Network ◽  
Construction Method ◽  
Data Driven ◽  
Regression Problem ◽  
Network Construction ◽  
Benchmark Datasets ◽  
Gene Regulatory ◽  
New Perspective

Abstract Background To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem. Results In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR. Conclusions We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms

2021 ◽  
Vol 30 (04) ◽  
pp. 2150022
Author(s):  
Sergio Peignier ◽  
Pauline Schmitt ◽  
Federica Calevro
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Data Driven ◽  
Classification Algorithms ◽  
New Family ◽  
Gene Regulatory ◽  
High Throughput Gene Expression ◽  
Inference Methods

Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

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

2020 ◽  
Author(s):  
Nikolas Bernaola ◽  
Mario Michiels ◽  
Pedro Larrañaga ◽  
Concha Bielza
Keyword(s):  
Human Brain ◽  
Bayesian Networks ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Search Algorithm ◽  
Experimental Tests ◽  
Brain Structures ◽  
Brain Atlas ◽  
Gene Regulatory ◽  
Using Data

AbstractWe 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.

scholarly journals scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes

2020 ◽  
Author(s):  
Larisa M. Soto ◽  
Juan P. Bernal-Tamayo ◽  
Robert Lehmann ◽  
Subash Balsamy ◽  
Xabier Martinez-de-Morentin ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Data Driven ◽  
Energy Landscapes ◽  
Cell Type ◽  
Cell Clustering ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Python Package

AbstractRecent progress in single-cell genomics has generated multiple tools for cell clustering, annotation, and trajectory inference; yet, inferring their associated regulatory mechanisms is unresolved. Here we present scMomentum, a model-based data-driven formulation to predict gene regulatory networks and energy landscapes from single-cell transcriptomic data without requiring temporal or perturbation experiments. scMomentum provides significant advantages over existing methods with respect to computational efficiency, scalability, network structure, and biological application.AvailabilityscMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.git

Design of Knowledge Bases for Plant Gene Regulatory Networks

2017 ◽  
pp. 207-223
Author(s):  
Eric Mukundi ◽  
Fabio Gomez-Cano ◽  
Wilberforce Zachary Ouma ◽  
Erich Grotewold
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Knowledge Bases ◽  
Plant Gene ◽  
Gene Regulatory

scholarly journals JRmGRN: joint reconstruction of multiple gene regulatory networks with common hub genes using data from multiple tissues or conditions

2018 ◽  
Vol 34 (20) ◽  
pp. 3470-3478 ◽  
Author(s):  
Wenping Deng ◽  
Kui Zhang ◽  
Sanzhen Liu ◽  
Patrick X Zhao ◽  
Shizhong Xu ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Hub Genes ◽  
Multiple Gene ◽  
Joint Reconstruction ◽  
Gene Regulatory ◽  
Using Data
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