26 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…
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…
Xavier Boyen, Nir Friedman, Daphne Koller
Dynamic Bayesian networks provide a compact and natural representation for complex dynamic systems. However, in many cases, there is no expert available from whom a model can be elicited. Learning provides an alternative approach for constructing models of dynamic systems. In this paper, we address some of the crucial…
Jianguo Ding, Pascal Bouvry
With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business…
Jacob Thomas-Hegarty, Stefan R. Pulver, V Anne Smith
Neural information flow describes the movement of activity between neurons or brain areas. Advances in experimental methods have allowed production of large amounts of observational data related to neuronal activity from the single-neuron to population level. Most current methods for analysing these data are based on…
Alec G. Sheffield, Sachira Denagamage, Mitchell P Morton, Anirvan S. Nandy + 1 more
Understanding how information dynamically flows within neural systems is a crucial problem in neuroscience. Traditional approaches often assume stationary or quasi-stationary functional networks, which fail to capture the time-varying dynamics of interactions among neural variables. To address this limitation, we…
Nabil Ghanmy, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara
— In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty. We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms. Then we will present different levels and methods of creating DBNs…
Joshua Brulé
This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models, dynamic Bayesian networks with continuous random variables and discrete children of continuous parents are capable of performing…
Rodrigo A. Collazo, Jim Q. Smith
A Dynamic Chain Event Graph (DCEG) provides a rich tree-based framework for modelling a dynamic process with highly asymmetric developments. An N Time-Slice DCEG (NT-DCEG) is a useful subclass of the DCEG class that exhibits a specific type of periodicity in its supporting tree graph and embodies a time-homogeneity…
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…
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…
Tapan Chowdhury, Anindya Maitra, Anjali Agarwal, Arundhuti Sur + 3 more
Cell growth is an intricate biological phenomenon that is closely regulated by the interplay between various growth factors and transcription factors. Signaling pathways are the main mediators in this event, which provide the driving force for mitosis or sometimes meiosis. However, when malfunctions occur within the…
Marco Scutari
Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper we will…
Chris J. Oates, Steven M. Hill, Sach Mukherjee
Recently, Li et al. (Bioinformatics 27(19), 2686-91, 2011) proposed a method, called Differential Equation-based Local Dynamic Bayesian Network (DELDBN), for reverse engineering gene regulatory networks from time-course data. We commend the authors for an interesting paper that draws attention to the close relationship…
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…
Jhonathan P. R. dos Santos, Samuel B. Fernandes, Roberto Lozano, Patrick J. Brown + 3 more
The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench] lines, which had been genotyped with 100,435…
Avi Pfeffer, Terry Tai
Systems such as sensor networks and teams of autonomous robots consist of multiple autonomous entities that interact with each other in a distributed, asynchronous manner. These entities need to keep track of the state of the system as it evolves. Asynchronous systems lead to special challenges for monitoring, as nodes…
Chixiang Chen, Libo Jiang, Ming Wang, Yaqun Wang + 6 more
Gene regulatory networks (GRNs) have been widely used as a fundamental tool to reveal the genomic mechanisms that underlie the organism’s response to environmental and developmental cues. Standard approaches infer GRNs as holistic graphs of gene co-expression, but such graphs cannot quantify how gene-gene interactions…
Jinyu Li, Yutong Lai, Chi Zhang, Qi Zhang
Various gene network models with distinct physical nature have been widely used in biological studies. For temporal transcriptomic studies, the current dynamic models either ignore the temporal variation in the network structure or fail to scale up to a large number of genes due to severe computational bottlenecks and…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…