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Dr. Algın and his collaborators’ MRI robotic brain intervention system project has received R&D support from the Turkish Institutes of Health (TÜSEB) and the German R&D companies MARVIS and NORAS.

Recent research by Prof. Dr. Oktay Algın and his collaborators has led to the development of MRI-compatible brain biopsy and ablation techniques. These innovations have received support from the Turkish Institutes of Health (TÜSEB), MARVIS—a manufacturer of MRI- compatible guidewires—and NORAS, a manufacturer of MRI coils. These studies will evaluate the ability to perform MRI-compatible […]

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scGraPhT: Merging Transformers and Graph Neural Networks for Single-Cell Annotation

We are proud to share our interdisciplinary and collaborative work at the Department of Electrical and Electronics Engineering and UMRAM of Bilkent University and the School of Medicine of Koç University on single-cell RNA sequencing (scRNA-seq) with graph- and transformer-based neural networks! Our article is now published in the Special Issue on Learning on Graphs […]

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New studies by Dr. Algin and his team on Diffusion Tensor Imaging and MR Cisternography have been published in SCI journals.

Oktay Algin and his colleagues conducted two studies published in two international scientific journals. The names of these studies are ‘Evaluation of the Glymphatic System in Rabbits Using Gadobutrol-Enhanced MR Cisternography With T1 and T2 Mapping’ and ‘Thalamo-insular cortex connections in the rat and human’. The related articles can be accessed at the following links: https://pubmed.ncbi.nlm.nih.gov/39746567/ https://pubmed.ncbi.nlm.nih.gov/39746567/

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Semantic Communication Over Channels with Insertions, Deletions, and Substitutions

Happy to share the first semantic communication framework on insertion-deletion-substitution (IDS) channels as published in our latest paper titled “Semantic Communication over Channels with Insertions, Deletions, and Substitutions” published in IEEE Communications Letters! A significant class of binary input channels are prone to synchronization errors, which are modeled as IDS channels. IDS channels occur in […]

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Text-RGNNs: Relational Modeling for Heterogeneous Text Graphs

Happy to share our latest paper titled “Text-RGNNs: Relational Modeling for Heterogeneous Text Graphs” published in IEEE Signal Processing Letters! Building on the foundational Text-graph convolutional Network (TextGCN), which represents corpus with heterogeneous text graphs, we addressed a key limitation: GCNs are inherently designed to operate within homogeneous graphs, potentially limiting their performance. To overcome […]

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