- author = "Zakaria Suliman Zubi and Marim Aboajela Emsaed",
- title = "Using sequence {DNA} chips data to Mining and Diagnosing Cancer Patients",
- journal = "International Journal of Computers",
- year = "2010",
- volume = "4",
- number = "4",
- month = "201--214",
- keywords = "genetic algorithms, genetic programming, DNA micro-array, Data Mining, Sequence Mining, Biological Database, Clustering, Classification, K-means.",
- ISSN = "1998-4308",
-
URL = "
http://www.naun.org/journals/computers/19-437.pdf",
-
URL = "
http://www.naun.org/main/NAUN/computers/2010.htm",
- size = "14 pages",
- 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 data set 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 analyse 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 concept 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 recognise the genes. The genes will be categorised 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.",
-
notes = "see also acmid = {1895290}
\cite{Zubi:2010:SMD:1895260.1895290}
Sirte University, Faculty of Science, Computer Science Department, Sirte, P.O Box 727, Libya,
Alfateh University, Faculty of Science, Computer Science Department, Tripoli, Libya, P,O Box 13210,
http://www.naun.org/journals/computers/",
