Bayesian computation methods for inferring regulatory network models using biomedical data

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

Abstract

The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.
Original languageEnglish
Title of host publicationTranslational Biomedical Informatics
Subtitle of host publicationA Precision Medicine Perspective
EditorsBairong Shen, Haixu Tang, Xiaoqian Jiang
Place of PublicationSingapore
PublisherSpringer
Pages289-307
Number of pages19
ISBN (Electronic)9789811015038
ISBN (Print)9789811015021
DOIs
Publication statusPublished - 3 Nov 2016

Publication series

NameAdvances in Experimental Medicine and Biology
PublisherSpringer
Volume939
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019

Keywords

  • Bayesian inference
  • Approximate Bayesian computation
  • Genetic regulation
  • Reverse engineering

Cite this

Tian, T. (2016). Bayesian computation methods for inferring regulatory network models using biomedical data. In B. Shen, H. Tang, & X. Jiang (Eds.), Translational Biomedical Informatics: A Precision Medicine Perspective (pp. 289-307). (Advances in Experimental Medicine and Biology; Vol. 939). Singapore: Springer. https://doi.org/10.1007/978-981-10-1503-8_12
Tian, Tianhai. / Bayesian computation methods for inferring regulatory network models using biomedical data. Translational Biomedical Informatics: A Precision Medicine Perspective. editor / Bairong Shen ; Haixu Tang ; Xiaoqian Jiang. Singapore : Springer, 2016. pp. 289-307 (Advances in Experimental Medicine and Biology).
@inbook{cba525e53b2c4e769195a7d74d3a6df5,
title = "Bayesian computation methods for inferring regulatory network models using biomedical data",
abstract = "The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.",
keywords = "Bayesian inference, Approximate Bayesian computation, Genetic regulation, Reverse engineering",
author = "Tianhai Tian",
year = "2016",
month = "11",
day = "3",
doi = "10.1007/978-981-10-1503-8_12",
language = "English",
isbn = "9789811015021",
series = "Advances in Experimental Medicine and Biology",
publisher = "Springer",
pages = "289--307",
editor = "Bairong Shen and Haixu Tang and Xiaoqian Jiang",
booktitle = "Translational Biomedical Informatics",

}

Tian, T 2016, Bayesian computation methods for inferring regulatory network models using biomedical data. in B Shen, H Tang & X Jiang (eds), Translational Biomedical Informatics: A Precision Medicine Perspective. Advances in Experimental Medicine and Biology, vol. 939, Springer, Singapore, pp. 289-307. https://doi.org/10.1007/978-981-10-1503-8_12

Bayesian computation methods for inferring regulatory network models using biomedical data. / Tian, Tianhai.

Translational Biomedical Informatics: A Precision Medicine Perspective. ed. / Bairong Shen; Haixu Tang; Xiaoqian Jiang. Singapore : Springer, 2016. p. 289-307 (Advances in Experimental Medicine and Biology; Vol. 939).

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

TY - CHAP

T1 - Bayesian computation methods for inferring regulatory network models using biomedical data

AU - Tian, Tianhai

PY - 2016/11/3

Y1 - 2016/11/3

N2 - The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.

AB - The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.

KW - Bayesian inference

KW - Approximate Bayesian computation

KW - Genetic regulation

KW - Reverse engineering

UR - http://www.scopus.com/inward/record.url?scp=84994646178&partnerID=8YFLogxK

U2 - 10.1007/978-981-10-1503-8_12

DO - 10.1007/978-981-10-1503-8_12

M3 - Chapter (Book)

AN - SCOPUS:84994646178

SN - 9789811015021

T3 - Advances in Experimental Medicine and Biology

SP - 289

EP - 307

BT - Translational Biomedical Informatics

A2 - Shen, Bairong

A2 - Tang, Haixu

A2 - Jiang, Xiaoqian

PB - Springer

CY - Singapore

ER -

Tian T. Bayesian computation methods for inferring regulatory network models using biomedical data. In Shen B, Tang H, Jiang X, editors, Translational Biomedical Informatics: A Precision Medicine Perspective. Singapore: Springer. 2016. p. 289-307. (Advances in Experimental Medicine and Biology). https://doi.org/10.1007/978-981-10-1503-8_12