Author

Natasha M. Setiabakti

Monash UniversityORCID 0000-0003-2017-561XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:imaging· 1

Frequent Co-Authors

Abigail S McGoverniD· 1 shared Juan Nunez-IglesiasiD· 1 shared Justin R. HamiltoniD· 1 shared Leila Shabani Mashcool· 1 shared Niklas BoknäsiD· 1 shared Pia LarssoniD· 1 shared Volga TarlaciD· 1 shared

Activity by Year

2024-2024 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Abigail S McGovernORCID

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1 paper

Recent·Most cited

Finding and Following: A deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivo

Abigail S McGovern, Pia Larsson, Volga Tarlac, Natasha M. Setiabakti, Leila Shabani Mashcool, Justin R. Hamilton, Niklas Boknäs, Juan Nunez-Iglesias

This paper introduces a deep learning-based pipeline for tracking platelets in thrombus formation, showing faster in vivo clotting and less passive platelet movement compared to ex vivo conditions.

Platelets · May 9, 2024Peer-reviewedBioinformatics