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Volkan Atalay

Researcher at Middle East Technical University

Publications -  80
Citations -  2304

Volkan Atalay is an academic researcher from Middle East Technical University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 20, co-authored 76 publications receiving 1629 citations. Previous affiliations of Volkan Atalay include Virginia Tech & Paris Descartes University.

Papers
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Proceedings ArticleDOI

Delaunay Triangulation based 3D Human Face Modeling from Uncalibrated Images

TL;DR: An algorithm for removing the degeneracies during triangulation by modifying the definition of the Delaunay cavity has also the effect of preserving the curavature in the face area.
Journal ArticleDOI

Subband domain coding of binary textual images for document archiving

TL;DR: In this work, a subband domain textual image compression method is developed and it is observed that very high compression ratios are obtained with this method.
Posted ContentDOI

DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations

TL;DR: This study proposes a large-scale DTI interaction prediction system, DEEPScreen, for early stage drug discovery, using convolutional deep neural networks and compared it with other deep learning based state-of-the-art DTI predictors on widely used benchmark datasets to indicate the effectiveness of the proposed deep learning approach.
Journal ArticleDOI

A signal transduction score flow algorithm for cyclic cellular pathway analysis, which combines transcriptome and ChIP-seq data.

TL;DR: A novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death.
Journal ArticleDOI

Identification of Novel Reference Genes Based on MeSH Categories

TL;DR: The results confirm that two or more reference genes should be used in combination for differential expression analysis of large-scale data obtained from microarray or next generation sequencing studies, and context dependent reference gene sets, as presented in this study, are required for normalization of expression data from diverse technological backgrounds.