Scientific Context of the Project

Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for storing and managing data with graph-like structures. Graph-like structures are frequently used to represent the complex and arbitrary relations among attributes of real world data, such as atoms (nodes) and bonds (edges) in chemical structures, proteins (nodes) and protein interactions (edges) in biological networks.  Most of the techniques, applied to optimize queries in graph databases, have been used in traditional databases, distributed systems or they are inspired from graph theory. However, their reuse in graph databases should take care of the main characteristics of graph databases, such as size of data, dynamic structure, highly interconnected data, and ability to efficiently access data relationships. Characteristics of nucleic acid based data and querying this data for targeting various nucleic acid based treatments can benefit the technological support provided by specifically designed big data query processing pipelines. This research should investigate big data query processing methods for multi-omics data for better understanding of the genetic factors underlying complex phenotypes.

Innovative Aspects of the Project

Despite enormous time and money costs to generate them, the biological multi-omics data is rather heterogeneous in nature and very big in size that prevents effective utilization by scientists in the life sciences domains. The project will develop novel integration and query processing methods to overcome these challenges.

Research Environment and Infrastructure

IZTECH Computer Engineering Department will provide candidate modern technological and research-oriented infrastructure. (https://dworld.iyte.edu.tr/)

Preferred Academic Background

Computer Science/Engineering

Required GRE Score

GRE Quantitative 157.00