Ahmet Süreyya Rifaioğlu

E-mail

rahmet@metu.edu.tr

Department

Graduate School of Natural and Applied Sciences

Scopus Author ID

57188820872

Drug Repurposing Approach to Identify Candidate Drug Molecules for Hepatocellular Carcinoma Baser, Tugce; Rifaioğlu, Ahmet Süreyya; Atalay, Mehmet Volkan; Atalay, Rengül (2024-09-01) Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer, with a high mortality rate due to the limited therapeutic options. Systemic drug treatments improve the patient’s life expectancy by only a few mon...
Disease Centric Large Scale De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks Ünlü, Atabey; Çevrim, Elif; Sarıgün, Ahmet; Ataş, Heval; Koyaş, Altay; Çelikbilek, Hayriye; Kahraman, Deniz Cansen; Olğaç, Abdurrahman; Rifaioğlu, Ahmet Süreyya; Doğan, Tunca (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10) Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. It is especially challenging to develop new drug-based treatments for complex diseases, such as various canc...
iBioProVis: interactive visualization and analysis of compound bioactivity space Dönmez, Ataberk; Rifaioğlu, Ahmet Süreyya; Atalay, Rengül; Atalay, Mehmet Volkan (Oxford University Press (OUP), 2020-08-15) SUMMARY: iBioProVis is an interactive tool for visual analysis of the compound bioactivity space in the context of target proteins, drugs and drug candidate compounds. iBioProVis tool takes target protein identifiers and, ...
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations Rifaioğlu, Ahmet Süreyya; Nalbat, Esra; Atalay, Mehmet Volkan (2020-03-07) The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, comp...
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens Zhou, N; et. al. (2019-11-19) Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the res...
iBioProVis: Interactive Visualization and Analysis of Compound Bioactivity Space Dönmez, Ataberk; Rifaioğlu, Ahmet Süreyya; Acar, Aybar Can; Doğan, Tunca; Martin, Maria Jesus; Atalay, Rengül; Atalay, Mehmet Volkan (2019-07-21) Visualization and interpretation of high-dimensional chemical compound and target space is critical for better understanding of the mechanisms of bioactivity space and drug discovery process. Here, we describe iBio...
CROssBAR: Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning Joshi, Vishal; Doğan, Tunca; Atalay, Rengül; Martin, Maria-Jesus; Saidi, Rabie; Zellner, Hermann; Volynkin, Vladimir; Sinoplu, Esra; Atas, Heval; Nightingale, Andrew; Rifaioğlu, Ahmet Süreyya; Atalay, Mehmet Volkan (2019-07-21) Biomedical information is scattered across different biological data resources, which are biologically related but only loosely linked to each other in terms of data connections. This hinders the applications of inte...
DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks Rifaioğlu, Ahmet Süreyya; Martin, Maria Jesus; Atalay, Rengül; Atalay, Mehmet Volkan (2019-05-14) Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conve...
ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature Dalkıran, Alperen; Rifaioğlu, Ahmet Süreyya; Dogan, Tunca; Atalay, Mehmet Volkan (2018-09-21) Background: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these stu...
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Rifaioğlu, Ahmet Süreyya; Martin, MJ; Atalay, Rengül; Atalay, Mehmet Volkan (2018-07-31) The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, exper...

Citation Formats