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Data-driven Gene Regulatory Network Inference based on Classification Algorithms

Abstract : Different paradigms of gene regulatory network inference have been proposed so far in the literature. The data-driven family is an important inference paradigm, that aims at scoring potential regulatory links between transcription factors and target genes, analyzing gene expression datasets. Three major approaches have been proposed to score such links relying on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference approaches, inspired on the regression based family, and based on classification algorithms. This paper advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the implementation and test of five new inference methods based on well-known classification algorithms shows that such an approach exhibits good quality results when compared to well-established paradigms.
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Contributor : Sergio Peignier <>
Submitted on : Wednesday, November 13, 2019 - 4:07:19 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:31 PM


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  • HAL Id : hal-02361914, version 1



Sergio Peignier, Pauline Schmitt, Federica Calevro. Data-driven Gene Regulatory Network Inference based on Classification Algorithms. 31st IEEE International Conference on Tools with Artificial Intelligence, Nov 2019, Portland, Oregon, United States. ⟨hal-02361914⟩



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