Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information.

Artificial neural networks have been combined with a rule based system to predict intron splice sites in the dicot plant Arabidopsis thaliana. A two step prediction scheme, where a global prediction of the coding potential regulates a cutoff level for a local prediction of splice sites, is refined by rules based on splice site confidence values, prediction scores, coding context and distances between potential splice sites. In this approach, the prediction of splice sites mutually affect each other in a non-local manner.

Cleaning the GenBank Arabidopsis thaliana data set.

Data driven computational biology relies on the large quantities of genomic data stored in international sequence data banks. However, the possibilities are drastically impaired if the stored data is unreliable. During a project aiming to predict splice sites in the dicot Arabidopsis thaliana, we extracted a data set from the A.

Training neural networks by means of genetic algorithms working on very long chromosomes.

In the neural network/genetic algorithm community, rather limited success in the training of neural networks by genetic algorithms has been reported. In a paper by Whitley et al. (1991), he claims that, due to "the multiple representations problem", genetic algorithms will not effectively be able to train multilayer perceptrons, whose chromosomal representation of its weights exceeds 300 bits.