Author

Yun Xiong

BGI Group (China)ORCID 0000-0002-8575-5415Unclaimed 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:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Cuicui Qiang· 2 shared Quanlei Zeng· 2 shared Shuixia Liu· 2 shared Ting FangiD· 2 shared Yi ZhangiD· 2 shared Yuying YuaniD· 2 shared Chaoqun Pang· 1 shared Dao‐Ling HuangiD· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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2 shared papersBioinformatics

Cuicui Qiang

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2 papers

Recent·Most cited

Accurate Name Entity Recognition for Biomedical Literatures: A Combined High-quality Manual Annotation and Deep-learning Natural Language Processing Study

Dao‐Ling Huang, Quanlei Zeng, Yun Xiong, Shuixia Liu, Chaoqun Pang, Menglei Xia, Ting Fang, Yanli Ma, Cuicui Qiang, Yi Zhang, Zhang Yu, Hong Li, Yuying Yuan

This study combines manual annotation and deep learning to improve name entity recognition in biomedical texts, achieving high F1-scores for gene, variant, disease, and species entities.

bioRxiv · Sep 15, 2021 · PreprintBioinformatics

Cancer SIGVAR: A semi-automated interpretation tool for germline variants of hereditary cancer-related genes

Hong Li, Shuixia Liu, Shuangying Wang, Quanlei Zeng, Yulan Chen, Ting Fang, Yi Zhang, Ying Zhou, Yu Zhang, Kaiyue Wang, Zhangwei Yan, Cuicui Qiang, Meng Qing Xu, Xianghua Chai, Yuying Yuan, , Yun Xiong

Cancer SIGVAR is a tool for interpreting germline cancer variants using 21 and 48 criteria, achieving high consistency in classification across benchmark databases.

Human Mutation · Feb 10, 2021Peer-reviewedGenetics