Ant Colony Optimization for Inferring Key Gene Interactions

06/06/2014
by   Khalid Raza, et al.
0

Inferring gene interaction network from gene expression data is an important task in systems biology research. The gene interaction network, especially key interactions, plays an important role in identifying biomarkers for disease that further helps in drug design. Ant colony optimization is an optimization algorithm based on natural evolution and has been used in many optimization problems. In this paper, we applied ant colony optimization algorithm for inferring the key gene interactions from gene expression data. The algorithm has been tested on two different kinds of benchmark datasets and observed that it successfully identify some key gene interactions.

READ FULL TEXT

page 6

page 7

research
11/11/2022

Inferring probabilistic Boolean networks from steady-state gene data samples

Probabilistic Boolean Networks have been proposed for estimating the beh...
research
03/10/2022

Bayesian Copula Directional Dependence for causal inference on gene expression data

Modelling and understanding directional gene networks is a major challen...
research
08/31/2019

Triclustering of Gene Expression Microarray Data Using Coarse-Grained Parallel Genetic Algorithm

Microarray data analysis is one of the major area of research in the fie...
research
10/30/2022

ISG: I can See Your Gene Expression

This paper aims to predict gene expression from a histology slide image ...
research
06/03/2019

Sea of Genes: Combining Animation and Narrative Strategies to Visualize Metagenomic Data for Museums

We examine the application of narrative strategies to present a complex ...
research
12/07/2010

Argudas: arguing with gene expression information

In situ hybridisation gene expression information helps biologists ident...
research
10/31/2019

Scaling structural learning with NO-BEARS to infer causal transcriptome networks

Constructing gene regulatory networks is a critical step in revealing di...

Please sign up or login with your details

Forgot password? Click here to reset