Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

Emmert‐Streib, Frank; Glazko, Galina V.; Altay, Gökmen; Simoes, Ricardo de Matos

doi:10.3389/fgene.2012.00008

Cited by 122 publications

(117 citation statements)

References 109 publications

(166 reference statements)

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“…Collection of sequential time points of transcriptomic data enables the use of network inference methods to reconstruct causative gene networks Windram et al, 2012;Lewis et al, 2015). Using network inference methods, it is thus possible to make predictions of regulatory interactions and how these interactions are influenced by different treatments or under different conditions (Hecker et al, 2009;Krouk et al, 2010;Emmert-Streib et al, 2012). Furthermore, networks can identify control hubs and enable prediction of the effect of genetic perturbations .…”

Section: Introductionmentioning

confidence: 99%

Changes in Gene Expression in Space and Time Orchestrate Environmentally Mediated Shaping of Root Architecture

et al. 2017

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Section: Introductionmentioning

confidence: 99%

Changes in Gene Expression in Space and Time Orchestrate Environmentally Mediated Shaping of Root Architecture

et al. 2017

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“…We focus here on statistical measures that compare a predicted network (or subnetwork) with the true one, as in the case of supervised network inference, some part of the true network is supposed to be available for training. In the general context of network inference, other performance measures have been proposed based either on functional annotations shared by genes/proteins or on topological properties of the inferred networks (see Emmert-Streib et al, 2012, for a survey).…”

Section: Evaluation Measuresmentioning

confidence: 99%

On protocols and measures for the validation of supervised methods for the inference of biological networks

Schrynemackers¹,

Küffner²,

Geurts³

2013

Front. Genet.

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Networks provide a natural representation of molecular biology knowledge, in particular to model relationships between biological entities such as genes, proteins, drugs, or diseases. Because of the effort, the cost, or the lack of the experiments necessary for the elucidation of these networks, computational approaches for network inference have been frequently investigated in the literature. In this paper, we examine the assessment of supervised network inference. Supervised inference is based on machine learning techniques that infer the network from a training sample of known interacting and possibly non-interacting entities and additional measurement data. While these methods are very effective, their reliable validation in silico poses a challenge, since both prediction and validation need to be performed on the basis of the same partially known network. Cross-validation techniques need to be specifically adapted to classification problems on pairs of objects. We perform a critical review and assessment of protocols and measures proposed in the literature and derive specific guidelines how to best exploit and evaluate machine learning techniques for network inference. Through theoretical considerations and in silico experiments, we analyze in depth how important factors influence the outcome of performance estimation. These factors include the amount of information available for the interacting entities, the sparsity and topology of biological networks, and the lack of experimentally verified non-interacting pairs.

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“…Before we start with our discussion, we would like to emphasize that here we consider gene regulatory networks to be causal networks [24][25][26]. That means whenever there is an interaction in a GRN between two genes, this is supposed to correspond to a biochemical interaction, e.g., a transcription regulation, a protein interaction, or a signaling event between gene products that can be experimentally validated.…”

Section: Introductionmentioning

confidence: 99%

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Emmert‐Streib

Dehmer

2018

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Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

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Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

Cited by 122 publications

References 109 publications

Changes in Gene Expression in Space and Time Orchestrate Environmentally Mediated Shaping of Root Architecture

Changes in Gene Expression in Space and Time Orchestrate Environmentally Mediated Shaping of Root Architecture

On protocols and measures for the validation of supervised methods for the inference of biological networks

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

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