Deep Neural Network for Supervised Inference of Gene Regulatory Network

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.

[1]  Kevin Kontos,et al.  Information-Theoretic Inference of Large Transcriptional Regulatory Networks , 2007, EURASIP J. Bioinform. Syst. Biol..

[2]  Jason Tsong-Li Wang,et al.  Semi-supervised prediction of gene regulatory networks using machine learning algorithms , 2015, Journal of Biosciences.

[3]  Byunghan Lee,et al.  Deep learning in bioinformatics , 2016, Briefings Bioinform..

[4]  Tripti Swarnkar,et al.  Handling Unlabeled Data in Gene Regulatory Network , 2013 .

[5]  Charles Elkan,et al.  Learning gene regulatory networks from only positive and unlabeled data , 2010, BMC Bioinformatics.

[6]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[7]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[8]  Dario Floreano,et al.  GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods , 2011, Bioinform..

[9]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[10]  Jiguo Cao,et al.  Modeling gene regulation networks using ordinary differential equations. , 2012, Methods in molecular biology.

[11]  Ming Chen,et al.  CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks , 2014, BMC Bioinformatics.

[12]  Nikhil Buduma,et al.  Fundamentals of deep learning , 2017 .

[13]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[14]  Jean-Philippe Vert,et al.  SIRENE: supervised inference of regulatory networks , 2008, ECCB.

[15]  Mark A. Ragan,et al.  Supervised, semi-supervised and unsupervised inference of gene regulatory networks , 2013, Briefings Bioinform..

[16]  Blagoj Ristevski,et al.  A survey of models for inference of gene regulatory networks , 2013 .

[17]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..