The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of COMPUTERS
Transactions ID Number: 19-437
Full Name: Zakaria Zubi
Position: Associate Professor
Age: ON
Sex: Male
Address: Sirte University, Faculty of Science, Computer Science Department Sirte, P.O Box 727, Libya,
Country: LIBYA
Tel: +218913752962
Tel prefix:
Fax:
E-mail address: zszubi@yahoo.com
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Title of the Paper: Using sequence DNA chips data to Mining and Diagnosing Cancer Patients
Authors as they appear in the Paper: Zakaria Suliman Zubi, Marim Aboajela Emsaed
Email addresses of all the authors: zszubi@yahoo.com, meemee_02@yahoo.com
Number of paper pages: 14
Abstract: Abstract: Deoxyribonucleic acid (DNA) micro-arrays present a powerful means of observing thousands of gene terms levels at the same time. They consist of high dimensional datasets, which challenge conventional clustering methods. The data's high dimensionality calls for Self Organizing Maps (SOMs) to cluster DNA micro-array data. The DNA micro-array dataset are stored in huge biological databases for several purposes [1]. The proposed methods are based on the idea of selecting a gene subset to distinguish all classes, it will be more effective to solve a multi-class problem, and we will propose a genetic programming (GP) based approach to analyze multi-class micro-array datasets. This biological dataset will be derived from multiple biological databases. The procedure responsible for extracting datasets called DNA-Aggregator. We will design a biological aggregator, which aggregates various datasets via DNA micro-array community-developed ontology based upon the con!
cept of semantic Web for integrating and exchanging biological data. Our aggregator is composed of modules that retrieve the data from various biological databases. It will also enable queries by other applications to recognize the genes. The genes will be categorized in groups based on a classification method, which collects similar expression patterns. Using a clustering method such as k-mean is required either to discover the groups of similar objects from the biological database to characterize the underlying data distribution.
Keywords: Key-Words: DNA micro-array, Data Mining, Sequence Mining, Biological Database, Genetic Programming, Clustering, Classification, K-means.
EXTENSION of the file: .doc
Special (Invited) Session: Sequence Mining in DNA chips data for Diagnosing Cancer Patients
Organizer of the Session: 635-205
How Did you learn about congress: Dr. Zaid Mohmed zszubi6@hotmail
IP ADDRESS: 41.254.2.15
