Research

The long-term goal of my research is to contribute to our understanding of the roles of alterations in complex diseases, such as cancer, by developing new methods and approaches for genomic technologies and personalized medicine. I’m interested in handling big data problems to help translate scientific research into medical practice with a focus on cancer and I develop methods and software to address challenges.

Answering these questions calls for interdisciplinary and close collaboration among biologists, clinicians, statisticians and computer scientists. Therefore, using my expertise in medical informatics and bioinformatics, I plan to help address them through developing integrative analysis methods and tools for biomedical data at genomics and clinical level.

My Ph.D. thesis “A Web Portal for Integrative Oncogenomics” (Samur et al., Plos One) resulted in the creation of canEvolve, a premier web portal that allows free access and tools for integrative oncogenomics to investigators. In addition to serving as a repository for TCGA and published oncogenomic data, canEvolve also provides users with integrative tools like dChip-GemiNI, an integrative tool for genome wide expression data of miRNAs and genes with feed forward loops consisting of transcription factors and miRNAs (Yan et al., Nucleic Acids Research), dosageEffect (Samur et al., BMC Genomics) which include our gene wise dosage effect score to measure concordant copy number and expression changes across cancer samples. Together these tools encompass integrative analysis of gene expression data with copy number profiles and microRNA profiles and patient survival. At present, canEvolve has been used by thousands of cancer researchers around the world. Moreover, I recently published RTCGAToolbox (Samur, Plos One) an R package that allows researches to access and analyze the Cancer Genome Atlas (TCGA) data systematically within the R platform. I am currently adding new features to the RTCGAToolbox to integrate multiple genomic data types to answer complex genomic hypothesis.