14 papers · ranked by Valyu relevance
Concha Bielza, Pedro Larrañaga
Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate…
Enzo Acerbi, Teresa Zelante, Vipin Narang, Fabio Stella
Background Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous…
Margarida Sousa, Alexandra M. Carvalho
Dynamic Bayesian networks (DBN) are powerful probabilistic representations that model stochastic processes. They consist of a prior network, representing the distribution over the initial variables, and a set of transition networks, representing the transition distribution between variables over time. It was shown that…
Peng Li, Chaoyang Zhang, Edward J Perkins, Ping Gong + 1 more
Background The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory…
Panuwat Trairatphisan, Andrzej Mizera, Jun Pang, Alexandru Adrian Tantar + 2 more
Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of…
Younyoung Choi, Robert J. Mislevy
An overarching mission of the educational assessment community today is strengthening the connection between assessment and learning. To support this effort, researchers draw variously on developments across technology, analytic methods, assessment design frameworks, research in learning domains, and cognitive, social…
Bin Yu, Jia-Meng Xu, Shan Li, Cheng Chen + 4 more
'Yan Zhang' 'Ming-Hui Wang'] Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single…
Haoni Li, Nan Wang, Ping Gong, Edward J Perkins + 1 more
Background Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure learning algorithm. REVEAL (REVerse Engineering ALgorithm) is one of the algorithms implemented for…
Dawood Behbehani, Nikos Komninos, Khalid Al-Begain, Muttukrishnan Rajarajan
'Muttukrishnan Rajarajan'] Cloud computing adoption has been increasing rapidly amid COVID-19 as organisations accelerate the implementation of their digital strategies. Most models adopt traditional dynamic risk assessment, which does not adequately quantify or monetise risks to enable business-appropriate…
James Hammond, V. Anne Smith
Bayesian networks (BNs) have been used for reconstructing interactions from biological data, in disciplines ranging from molecular biology to ecology and neuroscience. BNs learn conditional dependencies between variables, which best ‘explain’ the data, represented as a directed graph which approximates the…
Jiayao Zhang, Chunling Hu, Qianqian Zhang
A nonhomogeneous dynamic Bayesian network model, which combines the dynamic Bayesian network and the multi-change point process, solves the limitations of the dynamic Bayesian network in modeling non-stationary gene expression data to a certain extent. However, certain problems persist, such as the low network…
Cunlu Zou, Jianfeng Feng
Background In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among…
Nand Sharma, Joshua Millstein
Background Finding a globally optimal Bayesian Network using exhaustive search is a problem with super-exponential complexity, which severely restricts the number of variables that can feasibly be included. We implement a dynamic programming based algorithm with built-in dimensionality reduction and parent set…
Michael J. McGeachie, Hsun-Hsien Chang, Scott T. Weiss, Robert F. Murphy
'Robert F. Murphy'] Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous…