Yazar "Rifaioğlu, Ahmet Süreyya" için listeleme
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The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
Zhou, Naihui; Jiang, Yuxiang; Bergquist, Timothy R.; Lee, Alexandra J.; Kacsoh, Balint Z.; Crocker, Alex W.; Friedberg, Iddo; Rifaioğlu, Ahmet Süreyya (Bmc, 2019)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 ... -
CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations
Doğan, Tunca; Ataş, Heval; Joshi, Vishal; Atakan, Ahmet; Rifaioğlu, Ahmet Süreyya; Nalbat, Esra; Nightingale, Andrew; Saidi, Rabie; Volynkin, Vladimir; Zellner, Hermann; Atalay, Rengül Çetin; Martin, Maria; Atalay, Volkan (Oxford Academic, 2021)Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of ... -
DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks
Rifaioğlu, Ahmet Süreyya; Doğan, Tunca; Martin, Maria Jesus; Çetin-Atalay, Rengül; Atalay, Volkan (Nature Publishing Group, 2019)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 ... -
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations
Rifaioğlu, Ahmet Süreyya; Nalbat, Esra; Atalay, Volkan; Martin, Maria Jesus; Çetin-Atalay, Rengül; Doğan, Tunca (Royal Soc Chemistry, 2020)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, ... -
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; Çetin-Atalay, Rengül; Atalay, Volkan; Doğan, Tunca (BMC, 2018)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 ... -
iBioProVis: interactive visualization and analysis of compound bioactivity space
Dönmez, Ataberk; Rifaioğlu, Ahmet Süreyya; Acar, Aybar; Doğan, Tunca; Atalay, Rengül Çetin; Atalay, Volkan (NLM (Medline), 2020)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, optionally, ... -
Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants
Rifaioğlu, Ahmet Süreyya; Doğan, Tunca; Saraç, Ömer Sinan; Erşahin, Tülin; Saidi, Rabie; Atalay, Mehmet Volkan; Martin, Maria Jesus; Atalay, Rengül Çetin (Wiley, 2018)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 ... -
MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery
Rifaioğlu, Ahmet Süreyya; Atalay, R. Çetin; Kahraman, Deniz Cansen; Doğan, Tunca; Martín, María Jesús; Atalay, Volkan (Oxford University Press, 2021)Motivation: Identification of interactions between bioactive small molecules and target proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target effects. Due to the tremendous size of the ... -
Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl
Karakulak, Tülay; Rifaioğlu, Ahmet Süreyya; Rodrigues, Joao P. G. L. M.; Karaca, Ezgi (Frontiers Media S.A., 2021)Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the ... -
ProFAB—open protein functional annotation benchmark
Özdilek, A. Samet; Atakan, Ahmet; Özsarı, Gökhan; Acar, Aybar; Atalay, M. Volkan; Doğan, Tunca; Rifaioğlu, Ahmet Süreyya (Oxford University Press, 2023)As the number of protein sequences increases in biological databases, computational methods are required to provide accurate functional annotation with high coverage. Although several machine learning methods have been ... -
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
Rifaioğlu, Ahmet Süreyya; Ataş, Heval; Martin, Maria Jesus; Çetin-Atalay, Rengül; Atalay, Volkan; Doğan, Tunca (Oxford Univ Press, 2019)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, ... -
SLPred: a multi-view subcellular localization prediction tool for multi-location human proteins
Özsarı, Gökhan; Rifaioğlu, Ahmet Süreyya; Atakan, Ahmet; Tunca, Doğan; Martin, Maria Jesus; Atalay, Rengül Çetin; Atalay, Volkan (Oxford University Press, 2022)Accurate prediction of the subcellular locations (SLs) of proteins is a critical topic in protein science. In this study, we present SLPred, an ensemble-based multi-view and multi-label protein subcellular localization ... -
Transfer learning for drug–target interaction prediction
Dalkıran, Alperen; Atakan, Ahmet; Rifaioğlu, Ahmet Süreyya; Martin, Maria Jesús; Atalay, Rengül Çetin; Acar, Aybar Can; Doǧan, Tunca; Atalay, Volkan (Oxford University Press, 2023)MotivationUtilizing AI-driven approaches for drug-target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the ...
