Author

Andrei L. Turinsky

Hospital for Sick ChildrenORCID 0000-0002-5555-8461Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Developmental Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:clustering· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Michael BrudnoiD· 2 shared Amy P. WongiD· 1 shared Arun K RamaniiD· 1 shared Brian Thomas KalishiD· 1 shared Christine E. BeariD· 1 shared Cynthia E HawkinsiD· 1 shared Felix A. RatjeniD· 1 shared Henry QuachiD· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersBioinformatics

Michael BrudnoORCID

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Author profile →

3 papers

Recent·Most cited

Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity.

Henry Quach, Spencer Farrell, Ming Jia Michael Wu, Kayshani R. Kanagarajah, Joseph Wai-Hin Leung, Xiaoqiao Xu, Prajkta S. Kallurkar, Andrei L. Turinsky, Christine E. Bear, Felix A. Ratjen, Brian Thomas Kalish, Sidhartha Goyal, Theo J. Moraes, Amy P. Wong

This study creates a single-cell atlas of human fetal lung development, revealing epithelial cell plasticity and lineage hierarchies through scRNA-seq and spatial transcriptomics.

Nature Communications · Jul 13, 2024Peer-reviewedDevelopmental Biology

MetaFusion: A high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates.

Michael J. Apostolides, Yue Jiang, Mia Husić, Robert Siddaway, Cynthia E Hawkins, Andrei L. Turinsky, Michael Brudno, Arun K Ramani

MetaFusion improves fusion detection by combining multiple callers, achieving higher precision and recall through standardized annotation and graph clustering.

Bioinformatics · Apr 19, 2021Peer-reviewedBioinformatics

An Unsupervised Learning Method for Disease Classification Based on DNA Methylation Signatures

Mohammad Firouzi, Andrei L. Turinsky, Sanaa Choufani, Michelle T. Siu, Rosanna Weksberg, Michael Brudno

This paper introduces an unsupervised learning method to classify diseases using DNA methylation signatures, achieving high accuracy on known disorders and identifying a new subtype in autism.

bioRxiv · Dec 10, 2018 · PreprintBioinformatics