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Search · four archives
14 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…
Jinghang Liang, Jie Han
Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs). As a logical model, probabilistic Boolean networks (PBNs) consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics 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…
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…
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…
Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu + 1 more
'Edda Klipp'] Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and…
Pedro Juan Rivera Torres
We revisit Probabilistic Boolean Networks as trainable function approximators. The key obstacle, non-differentiable structural choices (which predictors to read and which Boolean operators to apply), is addressed by casting the PBN’s structure as a stochastic policy whose parameters are optimized with score-function…
Ou Wei, Zonghao Guo, Yun Niu, Wenyuan Liao
Background Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random…
Peican Zhu, Jinghang Liang, Jie Han
Background In a gene regulatory network (GRN), gene expressions are affected by noise, and stochastic fluctuations exist in the interactions among genes. These stochastic interactions are context dependent, thus it becomes important to consider noise in a context-sensitive manner in a network model. As a logical model…
Yihan He, Sheng Luo, Chao Fang, Gengchiau Liang
In this work, an innovative design model aimed at enhancing the efficacy of ground-state probabilistic logic with a binary energy landscape (GSPL-BEL) is presented. This model enables the direct conversion of conventional CMOS-based logic circuits into corresponding probabilistic graphical representations based on a…
Rafatul Faria, Jan Kaiser, Kerem Y. Camsari, Supriyo Datta
Directed acyclic graphs or Bayesian networks that are popular in many AI-related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits built out of probabilistic bits (p-bits), analogous to binary stochastic neurons of stochastic artificial neural networks. In order to satisfy…
Catarina Moreira, Emmanuel Haven, Sandro Sozzo, Andreas Wichert + 1 more
'Fenghua Wen'] In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The event log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The goal is to work out a decision model, which…