Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31108
Classifying Bio-Chip Data using an Ant Colony System Algorithm

Authors: Minsoo Lee, Yearn Jeong Kim, Yun-mi Kim, Sujeung Cheong, Sookyung Song


Bio-chips are used for experiments on genes and contain various information such as genes, samples and so on. The two-dimensional bio-chips, in which one axis represent genes and the other represent samples, are widely being used these days. Instead of experimenting with real genes which cost lots of money and much time to get the results, bio-chips are being used for biological experiments. And extracting data from the bio-chips with high accuracy and finding out the patterns or useful information from such data is very important. Bio-chip analysis systems extract data from various kinds of bio-chips and mine the data in order to get useful information. One of the commonly used methods to mine the data is classification. The algorithm that is used to classify the data can be various depending on the data types or number characteristics and so on. Considering that bio-chip data is extremely large, an algorithm that imitates the ecosystem such as the ant algorithm is suitable to use as an algorithm for classification. This paper focuses on finding the classification rules from the bio-chip data using the Ant Colony algorithm which imitates the ecosystem. The developed system takes in consideration the accuracy of the discovered rules when it applies it to the bio-chip data in order to predict the classes.

Keywords: classification, ant colony system, DNA chip data

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1179


[1] Barbara Comes, Arpad Kelemen. Probabilistic neural network classification for microarraydata. IEEE, 2003.
[2] J. Bala, J. Huang , H. Vafaiem K. DeJong and H. Wechsler. Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification. IJCAI conference, 1995.
[3] Jiawei Han, Micheline Kamber. Data Mining Concepts and Techniques. Morgan Kaufmann, 2001.
[4] Lizhuang Zhao, Mohammed J. Zaki, TriCluster: An Effecitive Algorithm for Mining Coferent Clusters in 3D Microarray Data. SIGMOD , Baltimore, Maryland, USA, June(2005).
[5] Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Vol.26, No.1, 1996.
[6] Marco Dorigo, and Luca Maria Gambardella. Ant colonies for the traveling salesman problem. BioSystems, 1997.
[7] Nicholas Holden and Alex A. Freitas. Web Page Classification with an Ant Colony Algorithm. Parallel Problem Solving from Nature - PPSN VIII, LNCS 3242, pages 1092-1102. Springer-Verlag, September 2004.
[8] Sorin Draghici. Data Analysis Tools for DNA Microarrays. Chapman & Hall, 2003.
[9] Wikipedia,
[10] Yi-Shiou Chen and Tah-Hsiung Chu, A Neural Network Classification Tree, IEEE, 1995.