Author

Bahar Taşdelen

Mersin ÜniversitesiORCID 0000-0001-8146-4912Unclaimed 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· 2 Assay:GWAS summary statistics· 2 Approach:machine learning (classical)· 2 Approach:deep learning· 1 Organism:human· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Ragıp Onur ÖZTORNACIiD· 2 shared Andrew P. MorrisiD· 1 shared Cemil ÇolakiD· 1 shared Erdal CoşguniD· 1 shared Hamzah SyediD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

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5 top collaborators · 6 mutual co-author links

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2 shared papersBioinformatics

Ragıp Onur ÖZTORNACIORCID

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2 papers

Recent·Most cited

BioinformaticsTurkiye Klinikleri Journal of BiostatisticsPeer-reviewedJan 1, 2025

Prediction of Polygenic Risk Score by Machine Learning and Deep Learning Methods in Genome-wide Association Studies

Ragıp Onur ÖZTORNACI, Erdal Coşgun, Cemil Çolak, Bahar Taşdelen

The paper compares machine learning and deep learning methods to classical approaches for calculating polygenic risk scores using GWAS data, finding ML methods provide more consistent case-control separation.

4 citations

GWAS summary statisticsmachine learning (classical)

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BioinformaticsJournal Of Big DataPeer-reviewedNov 30, 2023

The Use of Class Imbalanced Learning Methods on ULSAM Data to Predict the Case-Control Status in Genome-Wide Association Studies

Ragıp Onur ÖZTORNACI, Hamzah Syed, Andrew P. Morris, Bahar Taşdelen

This paper compares ML methods for predicting T2D status from GWAS data using imbalanced learning techniques, finding that MLP with SMOTE achieves the highest accuracy.

11 citations

humanGWAS summary statistics

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