A new classification method using array Comparative Genome Hybridization data, based on the concept of Limited Jumping Emerging Patterns

Abstract

Background: Classification using aCGH data is an important and insufficiently investigated problem in bioinformatics. In this paper we propose a new classification method of DNA copy number data based on the concept of limited Jumping Emerging Patterns. We present the comparison of our limJEPClassifier to SVM which is considered the most successful classifier in the case of high-throughput data. Results: Our results revealed that the classification performance using limJEPClassifier is significantly higher than other methods. Furthermore, we show that application of the limited JEP's can significantly improve classification, when strongly unbalanced data are given. Conclusion: Nowadays, aCGH has become a very important tool, used in research of cancer or genomic disorders. Therefore, improving classification of aCGH data can have a great impact on many medical issues such as the process of diagnosis and finding disease-related genes. The performed experiment shows that the application of Jumping Emerging Patterns can be effective in the classification of high-dimensional data, including these from aCGH experiments. © 2009 Gambin and Walczak; licensee BioMed Central Ltd.