Ahmet Süreyya Rifaioğlu

Graduate School of Natural and Applied Sciences
Scopus Author ID
iBioProVis: interactive visualization and analysis of compound bioactivity space
Dönmez, Ataberk; Rifaioğlu, Ahmet Süreyya; Acar, Aybar; DOĞAN, TUNCA; 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; Martin, Maria Jesus; Atalay, Rengül; DOĞAN, TUNCA (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...
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...
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...
DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks
Rifaioğlu, Ahmet Süreyya; Dogan, Tunca; 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; Martin, Maria Jesus; Atalay, Rengül; Atalay, Mehmet Volkan; Dogan, Tunca (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; Atas, H; Martin, MJ; Atalay, Rengül; Atalay, Mehmet Volkan; Dogan, T (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...
Automated Negative Gene Ontology Based Functional Predictions for Proteins with UniGOPred
Doğan, Tunca; Rifaioğlu, Ahmet Süreyya; Saidi, Rabi; Martin, Maria Jesus; Atalay, Mehmet Volkan; Atalay, Rengül (2018-07-07)
Functional annotation of biomolecules in the gene and protein databases is mostly incomplete. This is especially valid for multi-domain proteins. There is a grey area in the protein function data resources, where the truly...
Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants
Rifaioğlu, Ahmet Süreyya; Dogan, TUNCA; Sarac, Omer Sinan; ERSAHİN, Tulin; Saidi, Rabie; Atalay, Mehmet Volkan; MARTİN, Maria Jesus; Atalay, Rengül (2018-02-01)
Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and ...
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