Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in...

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Autores principales:	Kaixian Yu, Zihan Cui, Xin Sui, Xing Qiu, Jinfeng Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Bayesian network Bayesian network structure learning sequential Monte Carlo adaptive sequential Monte Carlo GRASP for BN structure learning biological network inference Genetics QH426-470
Acceso en línea:	https://doaj.org/article/f4d512a1e9d94907830b11973871dde9
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spelling	oai:doaj.org-article:f4d512a1e9d94907830b11973871dde92021-12-01T14:31:19ZBiological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo1664-802110.3389/fgene.2021.764020https://doaj.org/article/f4d512a1e9d94907830b11973871dde92021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.764020/fullhttps://doaj.org/toc/1664-8021Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.Kaixian YuZihan CuiXin SuiXing QiuJinfeng ZhangFrontiers Media S.A.articleBayesian networkBayesian network structure learningsequential Monte Carloadaptive sequential Monte CarloGRASP for BN structure learningbiological network inferenceGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian network Bayesian network structure learning sequential Monte Carlo adaptive sequential Monte Carlo GRASP for BN structure learning biological network inference Genetics QH426-470
spellingShingle	Bayesian network Bayesian network structure learning sequential Monte Carlo adaptive sequential Monte Carlo GRASP for BN structure learning biological network inference Genetics QH426-470 Kaixian Yu Zihan Cui Xin Sui Xing Qiu Jinfeng Zhang Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo
description	Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.
format	article
author	Kaixian Yu Zihan Cui Xin Sui Xing Qiu Jinfeng Zhang
author_facet	Kaixian Yu Zihan Cui Xin Sui Xing Qiu Jinfeng Zhang
author_sort	Kaixian Yu
title	Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo
title_short	Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo
title_full	Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo
title_fullStr	Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo
title_full_unstemmed	Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo
title_sort	biological network inference with grasp: a bayesian network structure learning method using adaptive sequential monte carlo
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f4d512a1e9d94907830b11973871dde9
work_keys_str_mv	AT kaixianyu biologicalnetworkinferencewithgraspabayesiannetworkstructurelearningmethodusingadaptivesequentialmontecarlo AT zihancui biologicalnetworkinferencewithgraspabayesiannetworkstructurelearningmethodusingadaptivesequentialmontecarlo AT xinsui biologicalnetworkinferencewithgraspabayesiannetworkstructurelearningmethodusingadaptivesequentialmontecarlo AT xingqiu biologicalnetworkinferencewithgraspabayesiannetworkstructurelearningmethodusingadaptivesequentialmontecarlo AT jinfengzhang biologicalnetworkinferencewithgraspabayesiannetworkstructurelearningmethodusingadaptivesequentialmontecarlo
_version_	1718405093382946816

Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo

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