Training knowledge-based neural networks to recognize genes in DNA sequences
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Characterizing RNA Secondary-Structure Features and Their Effects on Splice-Site Prediction
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
CISIS '08 Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems
Neural networks for prediction of nucleotide sequences by using genomic signals
WSEAS TRANSACTIONS on SYSTEMS
Splice Site Prediction Based on Characteristic of Sequential Motifs and C4.5 Algorithm
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Predicting Splice Site by Improved Bayesian Classifier
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 06
Human splice site identification with multiclass support vector machines and bagging
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A feature generation algorithm for sequences with application to splice-site prediction
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
| Hi-index | 0.00 |
Genes in complex organisms such as primates and humans are composed of regions that code for protein creation, called exons, and non-coding regions, called introns. During the transcription from the DNA template for later translating into amino acid chain of protein structure, introns are to be removed and exons are then joined to form a continuous messenger-RNA strand. Splice sites are the junctions between introns and exons. Accurate detection of splice sites from the fragments of DNA sequence is important to the success of gene prediction. In this paper, we propose a splice site prediction technique based on association analysis, named assoDNA. We apply association mining to each splice junction types, that is, exon/intron, intron/exon, and none of the two types. The frequent DNA patterns are then combined and prioritized with respect to their annotated confidence and support values. The final result of our method is a set of cascaded rules to be used for gene prediction. From the experimental results, our method can make a high recall prediction on a test set comparative to other classification-based methods.