Construction of Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix: A Maximum Entropy Rate Approach

DOI10.4208/eajam.080310.200910azbMath1287.65045OpenAlexW2060535971MaRDI QIDQ5406887

Nam-Kiu Tsing, Xiao Shan Chen, Xi Chen, Yang Cong, Wai-Ki Ching

Publication date: 4 April 2014

Published in: Unnamed Author (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4208/eajam.080310.200910a

zbMATH Keywords

convergence numerical example inverse problem Newton's method Markov chains conjugate gradient method Boolean networks genetic regulatory networks probabilistic Boolean networks transition-probability matrix genetic regulatory interactions

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Numerical mathematical programming methods (65K05) Minimax problems in mathematical programming (90C47) Numerical computation of solutions to systems of equations (65H10) Numerical analysis or methods applied to Markov chains (65C40) Iterative numerical methods for linear systems (65F10) Systems biology, networks (92C42)

Related Items (6)

A modified orthogonal matching pursuit for construction of sparse probabilistic Boolean networks ⋮ A geometric proximal gradient method for sparse least squares regression with probabilistic simplex constraint ⋮ On Construction of Sparse Probabilistic Boolean Networks ⋮ On Generating Optimal Sparse Probabilistic Boolean Networks with Maximum Entropy from a Positive Stationary Distribution ⋮ Sparse solution of nonnegative least squares problems with applications in the construction of probabilistic Boolean networks ⋮ An efficient Hessian based algorithm for singly linearly and box constrained least squares regression

Cites Work

This page was built for publication: Construction of Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix: A Maximum Entropy Rate Approach