Metabolic networks consist of interacting biochemical reaction chains (i.e., pathways) that are responsible for essential cellular processes such as energy production, DNA synthesis, and lipid breakdown. Reactions in these networks are connected to each other through chemical substances (e.g., glucose) that they consume and produce. Such substances are called “metabolites”, and with the recent advancements in biotechnology, it is possible to simultaneously measure the concentrations for thousands of metabolites in biofluids (e.g., blood, urine, etc.). Extraordinary changes in metabolite concentrations often point to physiological conditions. Metabolomics is the study of these concentration changes as well as interpretation of what their biochemical implications may be. Interpreting the concentration changes of large numbers of metabolites with respect to a metabolic network is complicated, time consuming, and cumbersome. In order to decrease and manage the complexity, most researchers usually focus on only a small select set of metabolites among thousands. This usually results in omitting a large chunk of the big picture, and makes the analysis conclusions local and limited. The goal of this project is to develop models, algorithms, and tools to automatically interpret large numbers of metabolite concentration changes in a holistic manner, and produce a set of biologically viable hypotheses explaining the observed changes. Besides, we aim to create health applications that will enable the use of the output from the above models and algorithms. ...
In database management systems, the estimation of selectivity is required to predict the cost of possible alternative execution plans for a given query. Many studies show that even very small inaccuracies in the cost estimates can cause the query optimizer to choose a suboptimal execution plan. With the explosion of the internet and text-based data, there is an ever greater need to evaluate queries involving string matching. The main difficulty in handling text data is due to the presence of unclean data, different spellings, and typographical errors. Consequently, there is an increasing demand of more support for good selectivity estimation techniques for approximate string queries. This project aims to develop new approaches for the selectivity estimation of approximate strings using data mining methods. ...
Predicting promising academic papers is useful for a variety of parties, including researchers, universities, scientiﬁc councils, and policymakers. Researchers may beneﬁt from such data to narrow down their reading list and focus on what will be important, and policymakers may use predictions to infer rising ﬁelds for a more strategic distribution of resources.This research focuses on developing novel techniques to predict a paper’s future impact (e.g., number of citations). We employ temporaland topological features derived from citation networks. In addition, we use a behavioral modeling approach to identify different families of papers in terms of their expected impact. We also adapt information diffusion paradigm into the citation networks to track the follow of ideas proposed by different papers. ...
Welcome to the Bioinformatics and Databases Lab at SEHIR. Our team consists of graduate and undergraduate research students and faculty mostly from Computer Science Department. However, due to the interdisciplinary nature of our research, we also collaborate with researchers from other domains, such as biology, genetics, and medicine. At a high level, our lab performs research in two major domains as summarized below.
Bioinformatics: We develop computational models and automated analysis methods for biological data. In particular, we focus on biochemical interactions (enzyme-enzyme and enzyme-metabolite) and metabolomics.
Data mining, data management, databases: We develop data mining and management algorithms to automatically predict variety of data pieces in diverse applications that range from predicting high impact scientific papers to mining graph and text patterns in biomedical databases.
We are looking for graduate and undergraduate students to participate in various research projects! Candidates with good programming and analytical thinking skills are encouraged to apply. Research assistantship with TUBITAK support is available for qualified promising graduate students. Please drop an email with your CV if you are interested in.