Author

Luyang Fang

University of GeorgiaORCID 0009-0003-2465-6864Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Animal Behavior & Cognition· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:dimensionality reduction· 2 Assay:spatial transcriptomics· 2 Assay:behavioral assay· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:diffusion model· 1

Frequent Co-Authors

Guo‐Cheng YuaniD· 2 shared Ping MaiD· 2 shared Wenxuan ZhongiD· 2 shared Beichen WangiD· 1 shared Haoran LuiD· 1 shared Jiazhang CaiiD· 1 shared Jiazhang Cai· 1 shared Motokazu TsujikawaiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersBioinformatics

Guo‐Cheng YuanORCID

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3 papers

Recent·Most cited

BioinformaticsNucleic Acids ResearchPeer-reviewedJun 6, 2026

Optimal Gene Panel Selection for Targeted Spatial Transcriptomics Experiments

Haoran Lu, Luyang Fang, O Zeng…·Ping Ma

This paper introduces ReconST, a method for optimal gene panel design in spatial transcriptomics, outperforming existing methods in reconstruction accuracy and spatial pattern preservation.

DOI 10.1093/nar/gkag621

mousespatial transcriptomics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintOct 7, 2025

SpaDiff: Denoising for Sequence-based Spatial Transcriptomics via Diffusion Process

Jiazhang Cai, Yongkai Chen, Luyang Fang…·Ping Ma

SpaDiff improves spatial transcriptomics by reversing RNA molecule displacement, enhancing spatial specificity and data integrity for downstream analyses.

2 citations

spatial transcriptomicsdiffusion model

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Animal Behavior & CognitionComputers in Biology and MedicinePeer-reviewedJan 29, 2025

Tensor analysis of animal behavior by matricization and feature selection

Beichen Wang, Jiazhang Cai, Luyang Fang…·Yuk Fai Leung

This study evaluates matricization and feature selection methods for analyzing multi-dimensional tensor behavioral data from zebrafish, showing improved classification performance and revealing new behavioral differences.

2 citations

zebrafishbehavioral assay

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