Reverse engineering of gene regulation models from multi-condition experiments
- Resource Type
- Authors
- Noel Kennedy; Paul Thompson; Werner Dubitzky; Huiru Zheng; Alexandru Mizeranschi
- Source
- CIBCB
- Subject
- Reverse engineering
Regulation of gene expression
0303 health sciences
Artificial neural network
business.industry
Computer science
Particle swarm optimization
Computational intelligence
computer.software_genre
Machine learning
03 medical and health sciences
0302 clinical medicine
Robustness (computer science)
Artificial intelligence
business
computer
030217 neurology & neurosurgery
030304 developmental biology
- Language
Reverse-engineering of quantitative, dynamic gene-regulatory network (GRN) models from time-series gene expression data is becoming important as such data are increasingly generated for research and other purposes. A key problem in the reverse-engineering process is the under-determined nature of these data. Because of this, the reverse-engineered GRN models often lack robustness and perform poorly when used to simulate system responses to new conditions. In this study, we present a novel method capable of inferring robust GRN models from multi-condition GRN experiments. This study uses two important computational intelligence methods: artificial neural networks and particle swarm optimization.