Author

Sheida Nabavi

University of ConnecticutORCID 0000-0002-5996-1020Unclaimed 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:clustering· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:dimensionality reduction· 1 Approach:graph neural network· 1 Assay:imaging· 1

Frequent Co-Authors

Clifford YangiD· 1 shared Dina AbdelhafiziD· 1 shared Jinbo BiiD· 1 shared Jun BaiiD· 1 shared Reda A.A. AmmariD· 1 shared Tianyu WangiD· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 7 mutual co-author links

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1 shared paperBioinformatics

Clifford YangORCID

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2 papers

Recent·Most cited

BioinformaticsBMC BioinformaticsPeer-reviewedJul 8, 2021

Single-Cell Classification Using Graph Convolutional Networks

Tianyu Wang, Jun Bai, Sheida Nabavi

This study introduces a deep learning model called sigGCN that combines graph convolutional networks with gene expression data to improve cell type classification accuracy.

52 citations

scRNA-seqdeep learning

𝕏 PostBluesky

BioinformaticsBMC BioinformaticsPeer-reviewedDec 1, 2020

Convolutional neural network for automated mass segmentation in mammography

Dina Abdelhafiz, Jinbo Bi, Reda A.A. Ammar…·Sheida Nabavi

The paper develops a Vanilla U-Net model for mass segmentation in mammography, achieving high accuracy and IOU metrics through data augmentation and transfer learning.

67 citationstop 5%

imagingdeep learning

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