Co-evolutionary Algorithm for Analyzing Gene Expression Data
- 10.2991/amsm-16.2016.28How to use a DOI?
- co-evolution; genetic algorithm; gene expression data; constrained optimization; nonlinear modeling; and computational biology; biostatistics and data analysis
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.
- © 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/).
Cite this article
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 -