In this paper, we present what we believe to be the first GPU-based implementation (using CUDA) for solving the gene regulatory network model inference problem. Our im-plementation uses differential evolution as its search engine, and adopts a power law system of differential equations (an S-System) for modelling the dynamics of the gene regulatory networks of our interest. Our preliminary results indicate that the use of GPUs produces an important reduction in the computational times required to solve this costly opti-mization problem. This could bring important benefits in Bioinformatics because of the many practical applications that the solution of this problem has
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
The isolation with migration (IM) model is important for studies in population genetics and phylogeo...
The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applications for dif...
Understanding the regulation of gene expression is one of the key problems in current biology. A pro...
Background: Gene regulatory networks (GRN) inference is an important bioinformatics problem in which...
Motivation: Mathematical modelling is central to systems and synthetic biology. Using simulations to...
Gene co-expression networks comprise one type of valuable biological networks. Many methods and tool...
<div><p>Gene co-expression networks comprise one type of valuable biological networks. Many methods ...
The implementation of efficient methods to infer mathematical models of biochemical phenomena from e...
Gene co-expression networks comprise one type of valuable biological networks. Many methods and tool...
The analysis of biological networks has become a major challenge due to the recent development of hi...
This chapter is concerned with modeling and simulating the dynamics of gene regulatory networks (GRN...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
In this paper, we present a fast and scalable method for computing eigenvector centrality using grap...
The use of Bioinformatic tools in routine clinical diagnostics is still facing a number of issues. T...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
The isolation with migration (IM) model is important for studies in population genetics and phylogeo...
The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applications for dif...
Understanding the regulation of gene expression is one of the key problems in current biology. A pro...
Background: Gene regulatory networks (GRN) inference is an important bioinformatics problem in which...
Motivation: Mathematical modelling is central to systems and synthetic biology. Using simulations to...
Gene co-expression networks comprise one type of valuable biological networks. Many methods and tool...
<div><p>Gene co-expression networks comprise one type of valuable biological networks. Many methods ...
The implementation of efficient methods to infer mathematical models of biochemical phenomena from e...
Gene co-expression networks comprise one type of valuable biological networks. Many methods and tool...
The analysis of biological networks has become a major challenge due to the recent development of hi...
This chapter is concerned with modeling and simulating the dynamics of gene regulatory networks (GRN...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
In this paper, we present a fast and scalable method for computing eigenvector centrality using grap...
The use of Bioinformatic tools in routine clinical diagnostics is still facing a number of issues. T...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
The isolation with migration (IM) model is important for studies in population genetics and phylogeo...
The use of Graphics Processing Units (GPUs) has recently witnessed ever growing applications for dif...