Identification of genetic networks by strategic gene disruptions and gene overexpressions under a Boolean model.

DOI10.1016/S0304-3975(02)00425-5zbMath1038.68090MaRDI QIDQ1874237

Tatsuya Akutsu, Satoru Kuhara, Satoru Miyano, Osamu Maruyama

Publication date: 22 May 2003

Published in: Theoretical Computer Science (Search for Journal in Brave)

zbMATH Keywords

Computational biologyBoolean networkGene expressionGenetic network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Genetics and epigenetics (92D10) Computational methods for problems pertaining to biology (92-08)

Related Items (16)

Modeling gene networks in \textit{Saccharomyces cerevisiae} based on gene expression profiles ⋮ Modeling delayed dynamics in biological regulatory networks from time series data ⋮ Efficient synthesis of a class of Boolean programs from I-O data: application to genetic networks ⋮ Analysing DNA microarray data using Boolean techniques ⋮ Reachability of higher-order logical control networks via matrix method ⋮ Robustness analysis of genetic regulatory networks affected by model uncertainty ⋮ Learning Boolean logic models of signaling networks with ASP ⋮ Learning from interpretation transition ⋮ Inference of gene regulatory networks using Bayesian nonparametric regression and topology information ⋮ Optimally learning social networks with activations and suppressions ⋮ Algorithms for Inference, Analysis and Control of Boolean Networks ⋮ Hamiltonian connectivity of diagonal grid graphs ⋮ Learning large-alphabet and analog circuits with value injection queries ⋮ On the robust stability of time-varying uncertain genetic regulatory networks ⋮ Completing Networks Using Observed Data ⋮ Learning any memory-less discrete semantics for dynamical systems represented by logic programs

Uses Software

• REVEAL

Cites Work

• Modeling the complexity of genetic networks: Understanding multigenic and pleiotropic regulation.
• Dynamical behaviour of biological regulatory networks. I: Biological role of feedback loops and practical use of the concept of the loop- characteristic state
• Queries and concept learning
• Biological Sequence Analysis
• Identifying gene regulatory networks from experimental data
• Unnamed Item

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