Biological data mining is the activity of finding significant information in biomolecular data. The significant information may refer to motifs, clusters, genes, and protein signatures. This paper presents an example of biological data mining: the recognition of promoters in DNA. We propose a two-level ensemble of classifiers to recognize E. Coli promoter sequences. The first-level classifiers include three Bayesian neural networks that learn from three different feature sets. The outputs of the first-level classifiers are combined in the second-level to give the final result. Empirical study shows that a precision rate of 92.2% is achieved, indicating an excellent performance of the proposed approach.
Feature Selection and Extraction for Graph Neural Networks