- Title
- Adaptive regulatory genes cardinality for reconstructing genetic networks
- Creator
- Chowdhury, Ahsan; Chetty, Madhu; Vinh, Nguyen
- Date
- 2012
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161501
- Identifier
- vital:12459
- Identifier
-
https://doi.org/10.1109/CEC.2012.6256462
- Abstract
- With the advent of microarray technology, researchers are able to determine cellular dynamics for thousands of genes simultaneously, thereby enabling reverse engineering of the gene regulatory network (GRN) from high-throughput time-series gene expression data. Amongst the various currently available models for inferring GRN, the S-System formalism is often considered as an excellent compromise between accuracy and mathematical tractability. In this paper, a novel approach for inferring GRN based on the decoupled S-System model, incorporating the new concept of adaptive regulatory genes cardinality, is proposed. Parameter learning for the S-System is carried out in an evolving manner using a versatile and robust Trigonometric Evolutionary Algorithm. The applicability and efficiency of the proposed method is studied using a well-known and widely studied synthetic network with various levels of noise, and excellent performance observed. Further, investigations of a 5 gene in-vivo synthetic biological network of Saccharomyces cerevisiae called IRMA, has succeeded in detecting higher number of correct regulations compared to other approaches reported earlier.
- Publisher
- IEEE
- Relation
- WCCI 2012 IEEE World Congress on Computational Intelligence
- Rights
- U.S. Government work not protected by U.S. copyright
- Rights
- This metadata is freely available under a CCO license
- Subject
- 09 Engineering; Gene regulatory network; Reverse engineering; S-System model; Cardinality
- Reviewed
- Hits: 1120
- Visitors: 1092
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|