Author

Abdulkerim Çapar

Istanbul Technical UniversityORCID 0000-0002-7110-5569Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Assay:imaging· 1 Organism:mouse· 1 Approach:variational autoencoder· 1

Frequent Co-Authors

Andrej KorenićiD· 1 shared Behçet Uğur TöreyıniD· 1 shared Bilal E. KermaniD· 1 shared Dursun Ali EkinciiD· 1 shared Sibel ÇimeniD· 1 shared Ufuk ÖzkayaiD· 1 shared Umut Engin AyteniD· 1 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 11 mutual co-author links

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1 shared paperBioinformatics

Andrej KorenićORCID

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2 papers

Recent·Most cited

Revisiting Reconstruction Likelihood: Variational Autoencoders for Biological and Biomedical Data Clustering

Andrej Korenić, Ufuk Özkaya, Abdulkerim Çapar

This paper shows that variational autoencoders can effectively cluster data by encoding cluster assignments in their latent space, using reconstruction likelihood to identify typical samples and anomalies.

bioRxiv · Apr 12, 2026 · PreprintBioinformatics

DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

Sibel Çimen, Abdulkerim Çapar, Dursun Ali Ekinci, Umut Engin Ayten, Bilal E. Kerman, Behçet Uğur Töreyın

DeepMQ quantifies myelin sheaths in fluorescence images of mouse stem cell-derived cells with 93.38% accuracy using a feature extraction and deep learning classification approach.

European Signal Processing Conference (EUSIPCO) · Sep 1, 2018Peer-reviewedNeuroscience