Author

Jun Song

Northeast Agricultural UniversityPrimary ORCID profile (0000-0002-7689-1722 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Jun SongiD1 paper · University of Manchester

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Assay:CRISPR screen· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Research Focus:software / tool· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Chen Cheng· 1 shared Guocheng LaniD· 1 shared Guoliang WangiD· 1 shared Huili WangiD· 1 shared Joshua W. K. HoiD· 1 shared Kuan-Li Ho· 1 shared Lu YuiD· 1 shared Meng WuiD· 1 shared

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2024-2024 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Chen Cheng

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1 paper

Recent·Most cited

Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygote with few-shot learning

Weizhong Zheng, Lu Yu, Guoliang Wang, Shaoxian Cao, Kuan-Li Ho, Jun Song, Chen Cheng, Joshua W. K. Ho, Xueqing Liu, Meng Wu, Zhonghua Liu, Huili Wang, Pentao Liu, Guocheng Lan, Yuanhua Huang

This paper introduces inDecay, a few-shot learning system for predicting CRISPR editing outcomes in cell lines and zygotes, achieving high accuracy with minimal data.

bioRxiv · Nov 8, 2024 · PreprintBioinformatics