Bioinformatics and Biostatistics Research Group - International Burch University

Bioinformatics and Biostatistics Research Group


The fields of bioinformatics aim to investigate questions about biological composition, structure, function, and evolution of molecules, cells, tissues, and organisms using mathematics, informatics, statistics, and computer science. Because these approaches allow large-scale and quantitative analyses of biological phenomena and data obtained from many disciplines, they can ask questions and achieve unique insights not imaginable before the genomic era. Both bioinformatics and biostatistics are frequently integrated into faculty laboratories, often with experimental studies as well, with bioinformatics emphasizing informatics and statistics, while bioinformatics emphasizes the development of theoretical methods, mathematical modeling, and computational simulation techniques to answer these questions. Examples of bioinformatics studies include analysis and integration of -omics data, prediction of protein function from sequence and structural information, and cheminformatics comparisons of protein ligands to identify off-target effects of drugs. Examples in computational biology include simulation of protein motion and folding and how proteins interact with each other. Research areas of bioinformatics are cancer gene expression, methylation, mutation, microRNA expression and mutation by using different related databases. Biostatistics are engaged in research on a wide variety of methodological problems. Major areas of methodological research include design and analysis of clinical trials, survival analysis, sequential methods, statistical genetics. Areas of application include cancer, community research, computational biology, the environment, genetic epidemiology, neurology, and psychiatry, among other areas.

Assist. Prof. Dr. Senol Dogan (contact person)
Student Assistant Hasan Emin Balkaya, Master student

Anis Cilic, Master student


Dogan S, Kurtovic-Kozaric A, Karli G. (2016). The Detection of Extremely High and Low Expressed Genes by EGEF Algorithm in Invasive Breast Cancer. J Biom Biostat 7: 286. doi:10.4172/2155-6180.1000286
Dogan S, Kurtovic-Kozaric A, Hajrovic A, Lisic M (2016) Comparison of MLL Fusion Genes Expression among the Cytogenetics Abnormalities of Acute Myeloid Leukemia and Their Clinical Effects. J Biom Biostat 7:312. doi: 10.4172/2155-6180.1000312
Mesut Karatas, Yusuf Turan, Kurtovic-Kozaric A and Senol Dogan. Analysis of Gaucher Disease Responsible Genes in Colorectal Adenocarcinoma. J Biom Biostat 2016, 7: 314. doi: 10.4172/2155-6180.1000314S
Senol Dogan,Anis Cilic,Amina Kurtovic-Kozaric and Fatih Ozturk.Detection of G-type density in promoter sequence of colon cancer oncogenes and tumor suppressor genes. Bioinformation. 2015; 11(6): 290–295. doi: 10.6026/97320630011290
DOGAN S. and Kurtovic-Kozaric A. 2015, Changes of Molecular, Cellular and Biological Activities According to microRNA-mRNA Interactions in Ovarian Cancer, Computational Molecular Biology, 5(4): 1-8
Senol Dogan. Differential Gene Expression and Methylation Signatures of Mixed Lineage Leukemias. NCRI 1-4 November in UK, Liverpool 2015. Conference proceeding
Senol Dogan, Gunay Karli, Adem Karadag, IOSRJEN) Computational Approach for Promoter Identification with data Mining Techniques. The Journal of Engineering 04(01):2250-3021 · January 2014
Senol Dogan. Comparison of Methylation Density in Different Cancer Types by Illumina Infinium HumanMethylation450 Methods. IWBBIO 1st International Work-Conference On Bioinformatics And Biomedical Engineering in Spain 2013 Granada. Conference proceeding


Senol Dogan, completed in December 2015; "Genomic characterization of mixed lineage leukemia"


Anis Cilic, Methylation analysis of Cytosine high density location in DNA sequences