Practical Guidelines for Incorporating Knowledge-Based and Data-Driven Strategies into the Inference of Gene Regulatory Networks

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.

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A prior knowledge based approach to infer gene regulatory networks

Proceedings of the International Symposium on Biocomputing - ISB '10 ◽

10.1145/1722024.1722069 ◽

2010 ◽

Cited By ~ 5

Author(s):

Md. Mahmudul Hasan ◽

Nasimul Noman ◽

Hitoshi Iba

Keyword(s):

Prior Knowledge ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Knowledge Based ◽

Gene Regulatory

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Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021500226 ◽

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.

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

10.1101/2020.12.30.424887 ◽

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

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