How to infer gene networks from expression profiles

By Mukesh Bansal, Vincenzo Belcastro, A. Ambesi-Impiombato, D. di Bernardo. Molecular Systems Biology, 2007

907 citations recorded by the source corpus.

Inferring, or ‘reverse‐engineering’, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse‐engineering algorithms for which ready‐to‐use software was available and that had been tested on experimental data sets. We show that reverse‐engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f6d5bdaa9ea5d90bf94608f68b1c8dd36d1ac843

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Citations in the corpus

Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, methodology): “Many are general to network inference approaches aiming to reconstruct GRN structure from mRNA profiles and are discussed in depth elsewhere (Penfold & Wild 2011, Marbach et al. 2012, Villaverde & Banga 2013, Oates & Mukherjee 2012), but it is worth emphasising a few key points that affect how we…”
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. (2009, positive): “Coexpression can be measured either by correlation or by a more robust measure called mutual information (Bansal et al., 2007).”
Systems biology of stem cell fate and cellular reprogramming (2009, background): “Extracting putative functional connections between elements in the parts list often requires some computational input, either by reverse engineering regulatory networks from specific experimentally derived datasets using sophisticated computational inference technique”
Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis (2010, methodology): “Model Framework: Genome-Scale Integrative Modeling Using PROM The construction of an integrated metabolic-regulatory network using PROM requires the following: (i) reconstructed genomescale metabolic network (19); (ii) regulatory network structure, consisting of transcription factors (TFs) and their”
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, background): “They are however also less informative for the prediction of edge directionality [3,26,27] and therefore make the regulatory network inference task more challenging.”
Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction (2009, methodology): “The resulting information is usually represented as a node–edge graph and stored in public databases such as Pathway Commons ((www.pathwaycommons.org); see Pathguide for a comprehensive list (Bader et al, 2006)) or in proprietary softwares from companies such as Ingenuity (Redwood City, CA, USA).”
Gene Expression Value Prediction Based on XGBoost Algorithm (2019, background): “These genes are called landmark genes, and the remaining genes are called target genes (Penfold and Wild 2011).”
Computational and analytical challenges in single-cell transcriptomics (2015, background): “Networks inferred using such an approach are typically undirected; that is, it is unknown which gene is upstream in the regulatory cascade 79 .”
Advantages and limitations of current network inference methods (2010, neutral): “In contrast to previous categorizations, our classification uses a combination of criteria that relates directly to the biological interpretation of the outcome rather than being merely data set relate”
Automated refinement and inference of analytical models for metabolic networks (2011, background): “ODEs are amenable to human interpretation because they are deterministic models and explicitly encode causal relationships [29], including feedback loops that are difficult to”
Single-cell transcriptome sequencing: recent advances and remaining challenges (2016, background): “The inherent gene expression variability between cells in scRNA-seq data can be used to infer gene regulatory networks (GRNs) 100–102 .”
Gene expression inference with deep learning (2015, methodology): “Such cross platforms generalizability implies the great potential of D-GEX to be applied to the LINCS program where training and prediction were also done separately on the microarray data and the L1000 data.”
Quantitative and logic modelling of gene and molecular networks (2015, methodology): “Most inference software was originally developed to infer the network from gene expression data (61,62), although information can be extracted from various molecular phenotypes (63).”