Author

Mingxuan Gao

Xiamen UniversityORCID 0000-0002-6619-6507Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:proteomics· 2 Assay:scRNA-seq· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:dimensionality reduction· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Rongshan YuiD· 3 shared Chuan‐Qi ZhongiD· 2 shared Jiahuai HaniD· 2 shared Jianwei ShuaiiD· 2 shared Qingzu He· 2 shared Wenxian YangiD· 2 shared Chaoyong James YangiD· 1 shared Chenxin LiiD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersBioinformatics

Rongshan YuORCID

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Author profile →

4 papers

Recent·Most cited

Dear-DIAXMBD: deep autoencoder for data-independent acquisition proteomics

Qingzu He, Chuan‐Qi Zhong, Xiang Li, Huan Guo, Yiming Li, Mingxuan Gao, Rongshan Yu, Xianming Liu, Fangfei Zhang, Tiannan Guo, Donghui Guo, Fangfu Ye, Jianwei Shuai, Jiahuai Han

Dear-DIAXMBD is a deep autoencoder method for analyzing DIA proteomics data, achieving superior performance across different species and instrument platforms.

bioRxiv · Aug 27, 2022 · PreprintBioinformatics

DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

Mingxuan Gao, Wenxian Yang, Chenxin Li, Yuqing Chang, Yachen Liu, Shun Wang, Qingzu He, Chuan‐Qi Zhong, Jianwei Shuai, Rongshan Yu, Jiahuai Han

DreamDIA-XMBD improves DIA proteomics analysis by using deep representation features for better identification and quantification than existing methods.

Communications Biology · Oct 14, 2021Peer-reviewedBioinformatics

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

Mingxuan Gao, Mingyi Ling, Xinwei Tang, Shun Wang, Xiao Xu, Ying Qiao, Wenxian Yang, Rongshan Yu

This study evaluates seven scRNA-seq data processing pipelines across nine datasets from five platforms, comparing their performance in runtime, resource use, and data consistency.

Briefings in Bioinformatics · May 18, 2020Peer-reviewedBioinformatics

SuperCT: A supervised-learning-framework to enhance the characterization of single-cell transcriptomic profiles

Peng Xie, Mingxuan Gao, Chunming Wang, Jianfei Zhang, Pawan Noel, Chaoyong James Yang, Daniel D. Von Hoff, Haiyong Han, Michael Q. Zhang, Wei Lin

SuperCT improves single-cell transcriptomic analysis by using a supervised classifier to accurately identify cell types from RNA expression profiles, outperforming traditional unsupervised methods.

Nucleic Acids Research · Feb 17, 2019Peer-reviewedBioinformatics