SIRENE: supervised inference of regulatory networks

Mordelet, Fantine; Vert, Jean-Philippe

doi:10.1093/bioinformatics/btn273

Cited by 167 publications

(191 citation statements)

References 23 publications

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“…Inferring accurate predictive models from few training examples is known to be challenging in machine learning, and in the extreme case where a vertex has no known neighbor during training, then no new edge can ever be predicted. However, the experimental results, reported by [8,27] and in Section 3, show that one can obtain very competitive results with local models in spite of this apparent difficulty.…”

Section: Graph Inference With Local Modelsmentioning

confidence: 97%

“…In this section we describe an approach that was proposed by [8] for the reconstruction of metabolic and PPI networks, and also successfully applied by [27] for regulatory network inference. The basic idea is very simple, an can be thought of as a "divide-and-conquer" strategy to infer new edges in a graph.…”

Section: Graph Inference With Local Modelsmentioning

confidence: 99%

“…For example, the approach was developed by [27] to reconstruct the gene regulatory network, i.e., to predict whether a transcription factor v regulates a gene v ′ , using a compendium of gene expression levels across a variety of experimental conditions as features. The paradigm seems particularly relevant in that case.…”

Section: Graph Inference With Local Modelsmentioning

confidence: 99%

“…Repeating this procedure k times leads to the possibility of predicting the labels for the k subsets, without ever predicting the label of a negative example that was used during training. This strategy was for example used in [27].…”

Section: Remarksmentioning

confidence: 99%

“…In contrast to these de novo methods, in this review we present a general approach to reconstruct biological networks using information about individual genes and proteins, based on supervised machine learning algorithms, as developed through a recent series of articles [45,43,46,3,6,42,8,27]. The graph inference paradigm we follow assumes that, besides the information about individual vertices (genes or proteins) used by de novo approaches, the graph we wish to infer is also partially known, and known edges can be used by the inference algorithm to infer unknown edges.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Reconstruction of Biological Networks by Supervised Machine Learning Approaches

Vert¹

2010

Elements of Computational Systems Biology

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Section: Graph Inference With Local Modelsmentioning

confidence: 97%

Section: Graph Inference With Local Modelsmentioning

confidence: 99%

Section: Graph Inference With Local Modelsmentioning

confidence: 99%

Section: Remarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reconstruction of Biological Networks by Supervised Machine Learning Approaches

Vert¹

2010

Elements of Computational Systems Biology

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Advances in computational and experimental approaches for deciphering transcriptional regulatory networks

Moeckel,

Mouratidis,

Chantzi

et al. 2024

BioEssays

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Understanding the influence of cis‐regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell‐type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression‐based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR‐Cas9‐based screening, which have significantly contributed to understanding TF binding preferences and cis‐regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis‐regulatory logic is analyzed. These computational advances have far‐reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

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Supervised Learning of Gene Regulatory Networks

Razaghi-Moghadam

Nikoloski

2020

CP Plant Biology

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Identifying the entirety of gene regulatory interactions in a biological system offers the possibility to determine the key molecular factors that affect important traits on the level of cells, tissues, and whole organisms. Despite the development of experimental approaches and technologies for identification of direct binding of transcription factors (TFs) to promoter regions of downstream target genes, computational approaches that utilize large compendia of transcriptomics data are still the predominant methods used to predict direct downstream targets of TFs, and thus reconstruct genome-wide gene-regulatory networks (GRNs). These approaches can broadly be categorized into unsupervised and supervised, based on whether data about known, experimentally verified gene-regulatory interactions are used in the process of reconstructing the underlying GRN. Here, we first describe the generic steps of supervised approaches for GRN reconstruction, since they have been recently shown to result in improved accuracy of the resulting networks? We also illustrate how they can be used with data from model organisms to obtain more accurate prediction of gene regulatory interactions.

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SIRENE: supervised inference of regulatory networks

Abstract: All data and programs are freely available at http://cbio. ensmp.fr/sirene.

Cited by 167 publications

References 23 publications

Reconstruction of Biological Networks by Supervised Machine Learning Approaches

Reconstruction of Biological Networks by Supervised Machine Learning Approaches

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks

Supervised Learning of Gene Regulatory Networks

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