Abstract — In this paper, a unified approach to infer gene regulatory networks using the S-system model is proposed. In order to discover the structure of large-scale gene regulatory networks, a simplified S-system model is proposed that enables fast parameter estimation to determine the major gene interactions. If a detailed S-system model is desirable for a subset of genes, a two-step method is proposed where the range of the parameters will be determined first using Genetic Programming and Recursive Least Square estimation. Then the exact values of the parameters will be calculated using a multi-dimensional optimization algorithm. Both downhill simplex algorithm and modified Powell algorithm are tested for multi-dimensional optimization....
Background\ud Reverse engineering gene networks and identifying regulatory interactions are integral...
Abstract- This paper describes an evolutionary method for identifying the gene regulatory network fr...
With the advent of the age of genomics, an increasing number of genes have been identified and thei...
Identification of the regulatory structures in genetic networks and the formulation of mechanistic m...
Gene regulatory network (GRN) reconstruction from high-throughput microarray data is an important pr...
[[abstract]]The inference of genetic regulatory networks from time-course data is one of the main ch...
[[abstract]]The inference of genetic regulatory networks from time-course data is one of the main ch...
Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challengin...
AbstractInferring regulatory networks in genetic systems and metabolic pathways is one of the most i...
The advent of microarray technology and the availability of high-throughput timeseries gene expressi...
AbstractInferring regulatory networks in genetic systems and metabolic pathways is one of the most i...
Reverse engineering of biochemical networks remains an important open challenge in computational sys...
Knowing every single component of a given biological sys-tem is not enough to understand the complex...
A Gene Regulatory Network (GRN) is the functional circuitry of a living organism that exhibits the r...
Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein...
Background\ud Reverse engineering gene networks and identifying regulatory interactions are integral...
Abstract- This paper describes an evolutionary method for identifying the gene regulatory network fr...
With the advent of the age of genomics, an increasing number of genes have been identified and thei...
Identification of the regulatory structures in genetic networks and the formulation of mechanistic m...
Gene regulatory network (GRN) reconstruction from high-throughput microarray data is an important pr...
[[abstract]]The inference of genetic regulatory networks from time-course data is one of the main ch...
[[abstract]]The inference of genetic regulatory networks from time-course data is one of the main ch...
Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challengin...
AbstractInferring regulatory networks in genetic systems and metabolic pathways is one of the most i...
The advent of microarray technology and the availability of high-throughput timeseries gene expressi...
AbstractInferring regulatory networks in genetic systems and metabolic pathways is one of the most i...
Reverse engineering of biochemical networks remains an important open challenge in computational sys...
Knowing every single component of a given biological sys-tem is not enough to understand the complex...
A Gene Regulatory Network (GRN) is the functional circuitry of a living organism that exhibits the r...
Motivation: The inference of biochemical networks, such as gene regulatory networks, protein–protein...
Background\ud Reverse engineering gene networks and identifying regulatory interactions are integral...
Abstract- This paper describes an evolutionary method for identifying the gene regulatory network fr...
With the advent of the age of genomics, an increasing number of genes have been identified and thei...