Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function

Haodi Jiang; Turki Turki; Jason T.L. Wang

doi:10.1109/ICMLA.2017.00-67

Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function

Haodi Jiang
, Turki Turki
, Jason T.L. Wang

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

In systems biology, two important regulatory networks are gene regulatory networks (GRNs) and regulatory networks of microRNAs (RNMs). A GRN is modeled as a directed graph in which a node represents a gene or transcription factor (TF), and an edge from a TF to a gene indicates that the TF regulates the expression of the gene. An RNM is modeled as a bipartite directed graph with two disjoint sets of nodes: A set of nodes that represent microRNAs (miRNAs) and a set of nodes that represent genes or TFs. Directed edges between these two sets of nodes represent miRNA-target interactions or TF-miRNA regulatory relations. In this paper, we present an approach to reverse engineering GRNs and RNMs using a dynamic Bayesian network and mutual information scoring function. Our approach is able to automatically infer both GRNs and RNMs from time series of expression data. Experimental results on different datasets show that our approach is more accurate than other time-series based network inference methods.

Original language	English (US)
Title of host publication	Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Editors	Xuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	761-764
Number of pages	4
ISBN (Electronic)	9781538614174
DOIs	https://doi.org/10.1109/ICMLA.2017.00-67
State	Published - Jan 1 2017
Event	16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico Duration: Dec 18 2017 → Dec 21 2017

Publication series

Name	Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume	2017-December

Other

Other	16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Country/Territory	Mexico
City	Cancun
Period	12/18/17 → 12/21/17

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications

Keywords

Bayesian networks
data mining
machine learning
mutual information
systems biology

Access to Document

10.1109/ICMLA.2017.00-67

Cite this

Jiang, H., Turki, T., & Wang, J. T. L. (2017). Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function. In X. Chen, B. Luo, F. Luo, V. Palade, & M. A. Wani (Eds.), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 (pp. 761-764). (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2017.00-67

Jiang, Haodi ; Turki, Turki ; Wang, Jason T.L. / Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. editor / Xuewen Chen ; Bo Luo ; Feng Luo ; Vasile Palade ; M. Arif Wani. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 761-764 (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017).

@inproceedings{8c6bfc3c68e249c28dc5a1fb247b7c3c,

title = "Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function",

abstract = "In systems biology, two important regulatory networks are gene regulatory networks (GRNs) and regulatory networks of microRNAs (RNMs). A GRN is modeled as a directed graph in which a node represents a gene or transcription factor (TF), and an edge from a TF to a gene indicates that the TF regulates the expression of the gene. An RNM is modeled as a bipartite directed graph with two disjoint sets of nodes: A set of nodes that represent microRNAs (miRNAs) and a set of nodes that represent genes or TFs. Directed edges between these two sets of nodes represent miRNA-target interactions or TF-miRNA regulatory relations. In this paper, we present an approach to reverse engineering GRNs and RNMs using a dynamic Bayesian network and mutual information scoring function. Our approach is able to automatically infer both GRNs and RNMs from time series of expression data. Experimental results on different datasets show that our approach is more accurate than other time-series based network inference methods.",

keywords = "Bayesian networks, data mining, machine learning, mutual information, systems biology",

author = "Haodi Jiang and Turki Turki and Wang, \{Jason T.L.\}",

year = "2017",

month = jan,

day = "1",

doi = "10.1109/ICMLA.2017.00-67",

language = "English (US)",

series = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "761--764",

editor = "Xuewen Chen and Bo Luo and Feng Luo and Vasile Palade and Wani, \{M. Arif\}",

booktitle = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",

address = "United States",

note = "16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 ; Conference date: 18-12-2017 Through 21-12-2017",

}

Jiang, H, Turki, T & Wang, JTL 2017, Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function. in X Chen, B Luo, F Luo, V Palade & MA Wani (eds), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 761-764, 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, Cancun, Mexico, 12/18/17. https://doi.org/10.1109/ICMLA.2017.00-67

Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function. / Jiang, Haodi; Turki, Turki; Wang, Jason T.L.
Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. ed. / Xuewen Chen; Bo Luo; Feng Luo; Vasile Palade; M. Arif Wani. Institute of Electrical and Electronics Engineers Inc., 2017. p. 761-764 (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function

AU - Jiang, Haodi

AU - Turki, Turki

AU - Wang, Jason T.L.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In systems biology, two important regulatory networks are gene regulatory networks (GRNs) and regulatory networks of microRNAs (RNMs). A GRN is modeled as a directed graph in which a node represents a gene or transcription factor (TF), and an edge from a TF to a gene indicates that the TF regulates the expression of the gene. An RNM is modeled as a bipartite directed graph with two disjoint sets of nodes: A set of nodes that represent microRNAs (miRNAs) and a set of nodes that represent genes or TFs. Directed edges between these two sets of nodes represent miRNA-target interactions or TF-miRNA regulatory relations. In this paper, we present an approach to reverse engineering GRNs and RNMs using a dynamic Bayesian network and mutual information scoring function. Our approach is able to automatically infer both GRNs and RNMs from time series of expression data. Experimental results on different datasets show that our approach is more accurate than other time-series based network inference methods.

AB - In systems biology, two important regulatory networks are gene regulatory networks (GRNs) and regulatory networks of microRNAs (RNMs). A GRN is modeled as a directed graph in which a node represents a gene or transcription factor (TF), and an edge from a TF to a gene indicates that the TF regulates the expression of the gene. An RNM is modeled as a bipartite directed graph with two disjoint sets of nodes: A set of nodes that represent microRNAs (miRNAs) and a set of nodes that represent genes or TFs. Directed edges between these two sets of nodes represent miRNA-target interactions or TF-miRNA regulatory relations. In this paper, we present an approach to reverse engineering GRNs and RNMs using a dynamic Bayesian network and mutual information scoring function. Our approach is able to automatically infer both GRNs and RNMs from time series of expression data. Experimental results on different datasets show that our approach is more accurate than other time-series based network inference methods.

KW - Bayesian networks

KW - data mining

KW - machine learning

KW - mutual information

KW - systems biology

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U2 - 10.1109/ICMLA.2017.00-67

DO - 10.1109/ICMLA.2017.00-67

M3 - Conference contribution

T3 - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017

SP - 761

EP - 764

BT - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017

A2 - Chen, Xuewen

A2 - Luo, Bo

A2 - Luo, Feng

A2 - Palade, Vasile

A2 - Wani, M. Arif

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017

Y2 - 18 December 2017 through 21 December 2017

ER -

Jiang H, Turki T, Wang JTL. Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function. In Chen X, Luo B, Luo F, Palade V, Wani MA, editors, Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 761-764. (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017). doi: 10.1109/ICMLA.2017.00-67

Reverse engineering regulatory networks in cells using a dynamic Bayesian network and mutual information scoring function

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