Reconstruct gene regulatory network using slice pattern model

A multi - objective evolutionary approach to reconstruct gene regulatory network using recurrent neural network model

With the advent of various data assaying techniques, gene expression time series data have become a useful resource to investigate the complex interactions occurring amongst the transcription factors and genes. While a number of methodologies have been de (GRN), the presence of high noise in gene expression data have made the estimation of non interactions among the genes an ill been proposed t...

Using Knowledge Driven Matrix Factorization to Reconstruct Modular Gene Regulatory Network

Reconstructing gene networks frommicro-array data can provide information on the mechanisms that govern cellular processes. Numerous studies have been devoted to addressing this problem. A popular method is to view the gene network as a Bayesian inference network, and to apply structure learning methods to determine the topology of the gene network. There are, however, several shortcomings with...

Dynamic Index Trading Using a Gene Regulatory Network Model

This paper presents a realistic study of applying a gene regulatory model to financial prediction. The combined adaptation of evolutionary and developmental processes used in the model highlight its suitability to dynamic domains, and the results obtained show the potential of this approach for real-world trading.

Estimation of Gene Regulatory Network Using Stochastic Differential Equation Model

A gene regulatory network model for control

The activity of a biological cell is regulated by interactions between genes and proteins. In artificial intelligence, this has led to the creation of developmental gene regulatory network (GRN) models which aim to exploit these mechanisms to algorithmically build complex designs. The emerging field of GRNs for control aims to instead exploit these natural mechanisms and this ability to encode ...