Instance-Based Learning Algorithms
Machine Learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Machine Learning
Applications of artificial intelligence in bioinformatics: A review
Expert Systems with Applications: An International Journal
Speeding up logistic model tree induction
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
| Hi-index | 12.05 |
This paper presents a study on polyadenylation site prediction, which is a very important problem in bioinformatics and medicine, promising to give a lot of answers especially in cancer research. We describe a method, called PolyA-iEP, that we developed for predicting polyadenylation sites and we present a systematic study of the problem of recognizing mRNA 3' ends which contain a polyadenylation site using the proposed method. PolyA-iEP is a modular system consisting of two main components that both contribute substantially to the descriptive and predictive potential of the system. In specific, PolyA-iEP exploits the advantages of emerging patterns, namely high understandability and discriminating power and the strength of a distance-based scoring method that we propose. The extracted emerging patterns may span across many elements around the polyadenylation site and can provide novel and interesting biological insights. The outputs of these two components are finally combined by a classifier in a highly effective framework, which in our setup reaches 93.7% of sensitivity and 88.2% of specificity. PolyA-iEP can be parameterized and used for both descriptive and predictive analysis. We have experimented with Arabidopsis thaliana sequences for evaluating our method and we have drawn important conclusions.