Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
Reads0
Chats0
TLDR
Probabilistic Boolean Networks (PBN) are introduced that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty.Abstract:
Motivation: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. Results: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks—a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.read more
Citations
More filters
Dissertation
Inférence de réseaux de régulation de gènes à partir de données dynamiques multi-échelles
TL;DR: Les resultats montrent que WASABI surmonte certaines limitations des approches existantes et sera certainement utile pour aider les biologistes dans l’analyse et l�’integration de leurs donnees.
Novel machine learning and correlation network methods for genomic data
TL;DR: The results indicated that MI networks could be safely replaced by correlation networks for stationary co-expression data and a novel bootstrap aggregated GLM predictor randomGLM (RGLM) was proposed that shares superior accuracy and good interpretability.
Proceedings ArticleDOI
Area-efficient two-dimensional architectures for finite field inversion and division
Zhiyuan Yan,Dilip V. Sarwate +1 more
TL;DR: This paper proposes a new reformulated EEA wherein the operations within the two pairs of polynomials are identical, and the two Pair of Polynomial can be concatenated into one pair.
Posted ContentDOI
Network Modeling and Inference of Peroxisome Proliferator-Activated Receptor Pathway in High fat diet-linked Obesity
Haswanth Vundavilli,Haswanth Vundavilli,Aniruddha Datta,Lokesh P. Tripathi,Kenji Mizuguchi,Kenji Mizuguchi +5 more
TL;DR: The authors' simulations have highlighted that GPCR and FATCD36 sub-pathways were aberrantly active in HFD mice and are therefore favorable targets for anti-obesity strategies and can help in inferring other pathways and deducing significant biological relationships.
Dissertation
Machine Learning and Network-Based Systems Toxicology Modeling of Chemotherapy-Induced Peripheral Neuropathy
TL;DR: This paper presents a network-based Model Code for Boolean Network Modeling of Intracellular Signaling and Gene Regulation in Peripheral Neurons of Dexanabinol and Bortezomib in Multiple Myeloma.
References
More filters
Book
The Origins of Order: Self-Organization and Selection in Evolution
TL;DR: The structure of rugged fitness landscapes and the structure of adaptive landscapes underlying protein evolution, and the architecture of genetic regulatory circuits and its evolution.
Journal ArticleDOI
Metabolic stability and epigenesis in randomly constructed genetic nets
TL;DR: The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary “genes”.
Journal ArticleDOI
Using Bayesian networks to analyze expression data
TL;DR: A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
Book
An introduction to Bayesian networks
TL;DR: The principal ideas of probabilistic reasoning - known as Bayesian networks - are outlined and their practical implications illustrated and are intended for MSc students in knowledge-based systems, artificial intelligence and statistics, and for professionals in decision support systems applications and research.