Identifying gene regulatory networks from experimental data
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Analyzing time series gene expression data
Bioinformatics
Genetic algorithms for gene expression analysis
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Gene finding and rule discovery with a multi-objective neural-genetic hybrid
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Data-Dependent Kernel Machines for Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Natural computing methods in bioinformatics: A survey
Information Fusion
Gene network inference using a swarm intelligence framework
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Multiagent Framework for Bio-data Mining
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Advances in Artificial Intelligence - Special issue on artificial intelligence in neuroscience and systems biology: lessons learnt, open problems, and the road ahead
Genetic Networks and Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hi-index | 0.00 |
Recent advances in biology (namely, DNA arrays) allow an unprecedented view of the biochemical mechanisms contained within a cell. However, this technology raises new challenges for computer scientists and biologists alike, as the data created by these arrays is often highly complex. One of the challenges is the elucidation of the regulatory connections and interactions between genes, proteins and other gene products. In this paper, a novel method is described for determining gene interactions in temporal gene expression data using genetic algorithms combined with a neural network component. Experiments conducted on real-world temporal gene expression data sets confirm that the approach is capable of finding gene networks that fit the data. A further repeated approach shows that those genes significantly involved in interaction with other genes can be highlighted and hypothetical gene networks and circuits proposed for further laboratory testing.