Author

Stephanie Sammut

University of MaltaORCID 0009-0006-1946-2661Unclaimed 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 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

David Čechák· 2 shared Dimosthenis TzimotoudisiD· 2 shared Eva MarsalkovaiD· 2 shared Katarína GrešováiD· 2 shared Panagiotis AlexiouiD· 2 shared David Farrugia· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 15 mutual co-author links

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2 shared papersBioinformatics

David Čechák

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2 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedJun 18, 2026

miRBind2 enables sequence-only prediction of miRNA binding and transcript repression

David Čechák, Dimosthenis Tzimotoudis, Stephanie Sammut…·Panagiotis Alexiou

miRBind2 is a deep learning model that predicts miRNA binding and transcript repression from sequence, outperforming existing methods with fewer parameters.

1 citation

deep learninggene regulatory network inference

𝕏 PostBluesky

BioinformaticsBioinformaticsPeer-reviewedApr 24, 2025

miRBench: novel benchmark datasets for microRNA binding site prediction that mitigate against prevalent microRNA Frequency Class Bias

Stephanie Sammut, Katarína Grešová, Dimosthenis Tzimotoudis…·Panagiotis Alexiou

The paper introduces miRBench, a benchmark dataset for microRNA binding site prediction that reduces miRNA frequency class bias, showing improved performance with a simple CNN model.

8 citations

statistical modelmachine learning (classical)

𝕏 PostBluesky