Abstract
Inferring gene regulatory network from gene expression data is a challenging task in system biology. Elucidating the structure of these networks is a machine-learning problem. Several approaches have been proposed to address this challenge using unsupervised semi-supervised and supervised methods. Semi-supervised and supervised methods use primordially SVM. Most supervised approaches infer local model where each local model is associated with one TF. In this work, we propose a global model to infer gene regulatory networks from experimental data using deep neural network architecture. We evaluate our method on DREAM4 multifactorial datasets. The obtained results show that prediction accuracy using deep neural network outperform SVM in all tested data.
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Daoudi, M., Meshoul, S. (2019). Deep Neural Network for Supervised Inference of Gene Regulatory Network. In: Chikhi, S., Amine, A., Chaoui, A., Saidouni, D.E. (eds) Modelling and Implementation of Complex Systems. MISC 2018. Lecture Notes in Networks and Systems, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-030-05481-6_11
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DOI: https://doi.org/10.1007/978-3-030-05481-6_11
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