A new classification method using array Comparative Genome Hybridization data, based on the concept of Limited Jumping Emerging Patterns
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 JEPs can significantly improve classification, when strongly unbalanced data are given.Conclusions: 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.
Tomasz Gambin Krzysztof Walczak
Faculty of Electronics and Information Technology of Warsaw University of Technology, Institute of Computer Science,Nowowiejska 15/19, Warsaw, 00-605, Poland
国际会议
The 7th Asia-Pacific Bioinformatics Conference(第七届亚太生物信息学大会)
北京
英文
706-716
2009-01-01（万方平台首次上网日期，不代表论文的发表时间）