Search · four archives
Search · four archives
22 papers · ranked by Valyu relevance
Yufei Xiao
Driven by the desire to understand genomic functions through the interactions among genes and gene products, the research in gene regulatory networks has become a heated area in genomic signal processing. Among the most studied mathematical models are Boolean networks and probabilistic Boolean networks, which are…
Panuwat Trairatphisan, Andrzej Mizera, Jun Pang, Alexandru Adrian Tantar + 2 more
'Alexandru Adrian Tantar' 'Thomas Sauter' 'Lars Kaderali'] Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of…
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…
Pedro Juan Rivera Torres, Chen Chen, Sara Rodríguez González, Orestes Llanes Santiago + 5 more
'Orestes Llanes Santiago' 'Jaroslaw Krzywanski' 'Marcin Sosnowski' 'Karolina Grabowska' 'Dorian Skrobek' 'Ghulam Moeen Uddin'] Probabilistic Boolean Networks (PBN) can model the dynamics of complex biological systems, as well as other non-biological systems like manufacturing systems and smart grids. In this…
Liu Zhonglin, Zhang Louxin
The modern understanding of Alzheimer’s disease as a disconnection syndrome presents the challenge of quantifying the directed influence between brain regions. To address this, we apply probabilistic Boolean networks to model effective brain connectivity for the first time, introducing a novel framework for analyzing…
Ifigeneia Apostolopoulou, Diana Marculescu
—Probabilistic Boolean Networks (PBNs) have been previously proposed so as to gain insights into complex dynamical systems. However, identification of large networks and of the underlying discrete Markov Chain which describes their temporal evolution, still remains a challenge. In this paper, we introduce an equivalent…
Christopher H. Fok, Chi-Wing Wong, Wai-Ki Ching
Boolean Networks Authors: ['Christopher H. Fok' 'Chi-Wing Wong' 'Wai-Ki Ching'] Boolean Network (BN) and its extension Probabilistic Boolean Network (PBN) are popular mathematical models for studying genetic regulatory networks. BNs and PBNs are also applied to model manufacturing systems, financial risk and healthcare…
Koichi Kobayashi, Kunihiko Hiraishi
One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of GRNs to gene therapy…
Sotiris Moschoyiannis, Evangelos Chatzaroulas, Vytenis Sliogeris, Yuhu Wu
The ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning (RL) has been proposed as a framework that solves a discrete-time optimal control problem cast as a Markov Decision Process. We focus on an…
Guiyun Xiao, Zheng‐Jian Bai, Wai‐Ki Ching
Probabilistic Boolean Networks play a remarkable role in the modelling and control of gene regulatory networks. In this paper, we consider the inverse problem of constructing a sparse probabilistic Boolean network from the prescribed transition probability matrix. We propose a modified orthogonal matching pursuit for…
Pedro Victori, Francesca M. Buffa
The current explosion of ’omics data has provided scientists with an unique opportunity to elucidate the inner workings of biological processes that remained opaque. For this, computational models are essential. Gene regulatory networks (GRN) have long been used as a way to integrate heterogeneous data into a discrete…
Dávid Deritei, Nina Kunšič, Péter Csermely
Biological systems are noisy by nature. This aspect is reflected in our experimental measurements and should be reflected in the models we build to better understand these systems. Noise can be especially consequential when trying to interpret specific regulatory interactions, i.e. regulatory network edges. In this…
Kathleen Johnson, Daniel Plaugher, David Murrugarra
Many processes in biology and medicine have been modeled using Markov decision processes which provides a rich algorithmic theory for model analysis and optimal control. An optimal control problem for stochastic discrete systems consists of deriving a control policy that dictates how the system will move from one state…
Stéphane Amarger, Didier Dubois, Henri Prade
An approach to reasoning with default rules where the proportion of exceptions, or more generally the probability of encountering an exception, can be at least roughly assessed is presented. It is based on local uncertainty propagation rules which provide the best bracketing of a conditional probability of interest…
Bingyu Jiang, Pierre Klemmer, Marek Ostaszewski
Boolean networks have become essential tools for modeling gene regulatory systems and understanding cellular decision-making processes, but their optimization for biological relevance remains challenging. This study presents a comprehensive benchmark comparison of three prominent Boolean network optimization…
Bahare Salmani, Joost-Pieter Katoen
This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on…
Punyashloka Debashis, Vaibhav Ostwal, Rafatul Faria, Supriyo Datta + 2 more
'Joerg Appenzeller' 'Zhihong Chen'] Abstract—Bayesian networks are powerful statistical models to understand causal relationships in real-world probabilistic problems such as diagnosis, forecasting, computer vision, etc. For systems that involve complex causal dependencies among many variables, the complexity of the…
Sampath Srinivas
The Noisy-Or model is convenient for describing a class of uncertain relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or model for Boolean variables. Here we generalize the model to nary input and output variables and to arbitrary functions other than the Boolean OR function. This…
Philipp-Maximilian Jacob, Alexei Lapkin
Is chemistry discoverable or can it only be invented? – this is the question of a computer scientist and a philosopher of science when looking at application of artificial intelligence methods for developing new chemical entities and new chemical transformations. This study confirms that, at least today, chemistry is…
Qi Zhang, Chang Liu, Stephen Wu, Ryo Yoshida
In the last few years, de novo molecular design using machine learning has made great technical progress but its practical deployment has not been as successful. This is mostly owing to the cost and technical difficulty of synthesizing such computationally designed molecules. To overcome such barriers, various methods…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath + 2 more
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are unintuitive, and products are unknown. Here we report new methods of CRN generation and analysis that overcome these…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath + 2 more
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are not well understood and products are unknown. Here we report new methods of CRN generation and analysis that overcome…