@inproceedings{b24040c14e16425a9f47f4cd0f5eec02,
title = "Reverse Engineering Gene Regulatory Networks Using Graph Mining",
abstract = "Reverse engineering gene regulatory networks (GRNs), also known as GRN inference, refers to the process of reconstructing GRNs from gene expression data. A GRN is modeled as a directed graph in which nodes represent genes and edges show regulatory relationships between the genes. By predicting the edges to infer a GRN, biologists can gain a better understanding of regulatory circuits and functional elements in cells. Many bioinformatics tools have been developed to computationally reverse engineer GRNs. However, none of these tools is able to perform perfect GRN inference. In this paper, we propose a graph mining approach capable of discovering frequent patterns from the GRNs inferred by existing methods. These frequent or common patterns are more likely to occur in true regulatory networks. Experimental results on different datasets demonstrate the good quality of the discovered patterns, and the superiority of our approach over the existing GRN inference methods.",
keywords = "Applications in biology and medicine, Graph mining, Network inference, Pattern discovery",
author = "Haodi Jiang and Turki Turki and Sen Zhang and Wang, {Jason T.L.}",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 ; Conference date: 15-07-2018 Through 19-07-2018",
year = "2018",
doi = "10.1007/978-3-319-96136-1_27",
language = "English (US)",
isbn = "9783319961354",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "335--349",
editor = "Petra Perner",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings",
address = "Germany",
}