Research

As a member of Sezerman Lab, I am working on multiple bioinformatics projects ranging from cancer genomics to post-translational modifications. Currently, my main research topic is using machine learning algorithms for exploring personal factors influencing strength of differential DNA methylation.

Additionally, I have been working in the analysis of clinical data provided by collaborator clinicians working on Duchenne muscular distrophy and Helicobacter pylori infections at Acibadem University.

You can find the latest list of my publications at Publications page or my Google Scholar profile.

In this page, I organised the past and present research projects I have been involved in, according to the institution.  You can view my involvement in these projects by clicking on the title.

Main Research Interests

  • Disease genomics
  • DNA Methylation
  • Integrative -omics analyses
  • Data analysis
  • Tool development
  • Machine learning

Sabanci University (PhD)

  • Investigation of factors driving differential methylation in human cancers via vertical data integration and machine learning

    My role includes design of the study and identification of common characteristics in differentially methylated regions for understanding their possible role in cancer aetiology with machine learning and integration of multiple types of -omics data.

    This project is supervised by Prof. Ugur Sezerman and currently ongoing.

  • Investigation of multi-level transcriptional regulation in disrupted co-expression networks

    My role in this project include design of the study and identification of DNA methylation, RNA, and miRNA-level regulation commonly seen in disrupted co-expression networks in human cancers. I am collaborating with a masters student (Begüm Özemek) in this project.

    This project is supervised by Prof. Ugur Sezerman and currently ongoing.

  • Developing a novel neddylation prediction tool

    I have performed the following tasks in this project: designing the study; collecting the dataset; deducing new features for neddylation prediction; performing statistical analyses and developing the prediction tool for neddylation site prediction.

    This project is completed and published as Prediction of neddylation sites from protein sequences and sequence-derived properties.

    Web server of this new method (NeddyPreddy) was implemented in Python and it is available at http: //neddypreddy.sabanciuniv.edu. Manuscript of the web-server is still under preparation and will be submitted to Nucleic Acids Research web server issue.

    This project was supervised by Prof. Uğur Sezerman.

  • Developing a sumoylation prediction method

    I have performed the following tasks for this project: improving the existing dataset; deducing new features for sumoylation prediction; performing statistical analyses and developing a the method for sumoylation site prediction.

    This project is completed and published as Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

    I originally started this project during my undergraduate education. Back then, I was trying a completely different approach by utilising 3D structure information in predicting potential sumoylation sites. I presented this method at HIBIT 2010 as SUMOtr: SUMOylation site prediction based on 3D structure and hydrophobicity.

    This project was supervised by Prof. Uğur Sezerman.

  • Deep learning architectures in computational biology

    For this project, I developed a Theano-based convolutional neural network tool and tested it on various biological datasets.

    This was before the release of TensorFlow, so, I don’t use this tool anymore.

    This project was supervised by Prof. Ugur Sezerman and Dr. Cem Meydan.

Imperial College London

  • Genome-wide exploration of transcriptional regulatory sequences in A. gambiae using genetic algorithm

    My role in this project was to develop a genetic programming-based motif mining methodology to identify the regulatory sequences in malarial mosquito Anopheles gambiae genome.

    This project was supervised by Dr. Bob MacCallum and Prof. George K. Christophides.

  • Defining protein networks regulating cell-cell adhesion using data mining techniques

    My role in this project was to compare various clustering methods and perspectives to create an efficient workflow for phenotype screens.

    This project was supervised by Dr. Luis Pizarro, Dr. Vania M M Braga, and Dr. Alessandro Russo .

  • CNA Express: an internet based application for the integrative analysis of copy number aberration and gene expression data sets

    My role in this project was to develop Amazon Web Services EC2 cloud computing back-end of the application.

    This project was a team effort with Daniel Homola, Miriam Leon, and Emilie Wilkie.

    This project was supervised by Geraint Barton, Dr. Chris Barnes, Dr. Mark Woodbridge, and Prof. Gerry Thomas.

Brigham and Women's Hospital / Harvard - MIT Health Sciences and Technology

Sabanci University (BSc)