Fuzzy clusterIng of gene expressIon data Systems Biology for Translational Medicine Prospective Project Partnership Meeting Fuzzy clusterIng of gene expressIon data Necla Koçhan Advisor: G. Yazgı Tütüncü Faculty of Arts and Sciences, Department of Mathematics

Field of Study / Technical Experience Keywords: Gene expression data analysis, fuzzy clustering, bioinformatics, Bayes theorem General Information: With the help of clustering it is easy to determine which genes are expressed (active) or not expressed (inactive) in normal or cancerous tissue. Thus, biologists may particularly focus on some groups of genes that result in trouble for any patient.

Projects A brief summary of completed and/or ongoing projects The aim of the project: A new fuzzy gene-based clustering method R programming language and a new clustering package for gene expression. simulated data sets and real gene expression data sets will be applied. tests for different scenarios and the performance of newly developed method will be compared to the performance of existing clustering techniques.

Project Idea / Contribution Fields Proposed project title: Fuzzy Bayes Clustering of Gene Expression Data List of the fields you may participate and/or techniques you may offer: Statistical Bayes approaches Fuzzy C-Means Clustering

Contact Details Assoc. Prof. Dr. G. Yazgı Tütüncü & Necla Koçhan Faculty of Arts and Sciences Departmant of Mathematics E-mails: yazgitutuncu@gmail.com necla.kayaalp@gmail.com