Develops computational models using genome datasets to study gene regulation and identify hypotheses for genomic medicine.
Next-generation sequencing technologies have revolutionized biological research and provided unique opportunities to study broad and novel questions about the regulation of gene expression. With these technologies, there has been an exponential increase in the types and amount of high-throughput datasets pertaining to the dynamics of gene expression. These data include gene expression data and genome-wide maps of nucleosome occupancy and open chromatin, epigenetic marks and transcription factor binding sites in cells and organisms under various experimental conditions. In my lab, we develop computational models to take advantage of genomics datasets to study the dynamics and mechanisms of transcriptional gene regulation and identify testable hypotheses for genomic medicine.
2009 - NSF Computing Innovation Postdoctoral Fellowship
2009 - Travel Award to attend International Society for Computational Biology conference ISMB
2009 - Travel Award to attend SIAM Data Mining Conference SDM
2007 - Best Applications Paper, ACM Knowledge Discovery and Data mining conference(Sig-KDD’2007)
2007 - Travel Award to attend Institute for Pure and Applied Mathematics (IPAM) workshop, UCLA
1998 - 2003 - Dean’s Honors, Bilkent University, Turkey,
1998 - 2003 - Full scholarship, Bilkent University, Turkey
Machine learning is essential to extracting knowledge from data. It focuses on prediction that can be used to make decisions for future...
Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor (https://bioconductor.org) is...
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