Localized neural network based distributional learning for knowledge discovery in protein databases

We investigate the application of localized neural network-based distributional learning techniques for characterizing interesting groups and potentially new types of disorder proteins. Instead of employing a single autoassociator model for learning global distributions of ordered and disordered classes, clustering-based partitioning techniques are first applied independently to both ordered and disordered labeled data set to identify regions of similar characteristics. Subsequently, local autoassociators are employed on labeled data to learn distribution of each cluster. These local autoassociators are used in testing phase to assign each tuple from the unlabeled data set to the cluster closest in distributional sense. Obtained partitions are analyzed for the presence and frequency of the expert-annotated keywords. Frequency comparison is applied to provide insight of keywords sensitive to the distribution heterogeneity and disorder/order labeling. Experimental results on a labeled database of confirmed order and disorder proteins and unlabeled data extracted from SWISS_PROT database are consistent with related literature and can provide further insight into relationship between protein similarity, keyword labeling and the disorder property.