Co-evolutionary Algorithm for Analyzing Gene Expression Data

DOI

10.2991/amsm-16.2016.28How to use a DOI?

Keywords

co-evolution; genetic algorithm; gene expression data; constrained optimization; nonlinear modeling; and computational biology; biostatistics and data analysis

Abstract

We investigate the employment of the co-evolutionary genetic algorithm (CoGA) as a search mechanism in a support system for designing the prediction, functionality and interaction of expression level in population of gene expressions. To correctly identify interactions between various experimental conditions or expression levels, we proposed a fitness function which is a metric on two randomly chosen expressed populations and integrated for the whole population. Our result indicates that expression level variability is not simply the manifestation of noise in the system, but instead it is probably the results of processes involving stochastic state transitions. These results finally suggest that even if genes in a population are independent, the number of proteins (and/or mRNAs) more likely co-regulated.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jimbo H. Claver
AU  - Isidore. S. Ngongo
PY  - 2016/05
DA  - 2016/05
TI  - Co-evolutionary Algorithm for Analyzing Gene Expression Data
BT  - Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling
PB  - Atlantis Press
SP  - 120
EP  - 124
SN  - 2352-538X
UR  - https://doi.org/10.2991/amsm-16.2016.28
DO  - 10.2991/amsm-16.2016.28
ID  - Claver2016/05
ER  -