Author

Özge Tatlı

Lund UniversityORCID 0000-0002-2530-855XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Approach:experimental assay· 1 Organism:human· 1 Research Focus:protein folding & dynamics· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Ahmad Nasimian· 2 shared Julhash Uddin KaziiD· 2 shared Lars RönnstrandiD· 2 shared Saleena YounusiD· 2 shared Ahmet KatıiD· 1 shared Baran DingiloğluiD· 1 shared Elek TelekiD· 1 shared Emma U. HammarlundiD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBioinformatics

Ahmad Nasimian

Mutual co-authors in cluster

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3 papers

Recent·Most cited

X-ray Crystallographic and Hydrogen Deuterium Exchange Studies Confirm Alternate Kinetic Models for Homolog Insulin Monomers

Esra Beyarslan Ayan, Miray Türk, Özge Tatlı, Sevginur Bostan, Elek Telek, Baran Dingiloğlu, Gizem Dinler Doğanay, Muhammed İkbal Alp, Ahmet Katı, Hasan DeMirci

The paper uses X-ray crystallography and hydrogen-deuterium exchange to study insulin analog dynamics, showing faster deuterium exchange and structural insights into modified insulin monomers.

PLoS ONE · Apr 21, 2025Peer-reviewedBiophysics

Understanding the characteristic behavior of the wild-type and mutant protein structure of FLT3 protein by computational methods

Saleena Younus, Özge Tatlı, Ahmad Nasimian, Julhash Uddin Kazi, Lars Rönnstrand

This study uses computational methods to analyze the structural behavior of FLT3 protein mutants in AML, identifying key structural changes and their implications for therapeutic targeting.

bioRxiv · Apr 18, 2024 · PreprintBioinformatics

A Clear, Legible, Explainable, Transparent, and Elucidative (CLETE) Binary Classification Platform for Tabular Data

Ahmad Nasimian, Saleena Younus, Özge Tatlı, Emma U. Hammarlund, Kenneth J. Pienta, Lars Rönnstrand, Julhash Uddin Kazi

The paper introduces a CLETE binary classification platform for tabular data that offers explainable, transparent, and modifiable machine learning models with various feature selection, sampling, and evaluation methods.

bioRxiv · Jun 20, 2023 · PreprintBioinformatics