1. 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

2. 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.

3. 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.

4. 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

5. Contact Details Assoc. Prof. Dr. G. Yazgı Tütüncü & Necla Koçhan
Faculty of Arts and Sciences
Departmant of Mathematics s: