Author

Zhe Xing

Fifth Affiliated Hospital of Zhengzhou UniversityORCID 0000-0002-7419-2012Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genetics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 4 Organism:human· 4 Research Focus:differential expression· 3 Research Focus:benchmarking· 2 Research Focus:gene regulatory network inference· 2 Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1

Frequent Co-Authors

Siyuan WengiD· 4 shared Xinwei HaniD· 4 shared Zaoqu LiuiD· 4 shared Chunguang GuoiD· 3 shared Hui XuiD· 3 shared Libo WangiD· 3 shared Jian ZhangiD· 2 shared Long LiuiD· 2 shared

Activity by Year

2022-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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Focal author (center)Selected co-authorORCID co-authorUnlinked name

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4 shared papersBioinformatics

Siyuan WengORCID

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Author profile →

4 papers

Recent·Most cited

BEST: a web application for comprehensive biomarker exploration on large-scale data in solid tumors

Zaoqu Liu, Long Liu, Siyuan Weng, Hui Xu, Zhe Xing, Yuqing Ren, Xiaoyong Ge, Libo Wang, Chunguang Guo, Lifeng Li, Quan Cheng, Peng Luo, Jian Zhang, Xinwei Han

BEST provides a web tool for comprehensive biomarker analysis in solid tumors using large-scale, multi-cohort data with standardized annotations and diverse analytical functions.

Journal Of Big Data · Nov 1, 2023Peer-reviewedGenomics

SurvivalML: an integrative platform for the discovery and exploration of prognostic models in multi-center cancer cohorts

Zaoqu Liu, Hui Xu, Long Liu, Siyuan Weng, Zhe Xing, Yuqing Ren, Xiaoyong Ge, Libo Wang, Chunguang Guo, Shuang Chen, Quan Cheng, Peng Luo, Jian Zhang, Xinwei Han

SurvivalML is a web platform that helps develop and validate prognostic models across multi-center cancer datasets by providing tools for data processing, model training, and evaluation.

bioRxiv · Oct 25, 2022 · PreprintBioinformatics

Comprehensive machine-learning survival framework develop a consensus model in large scale multi-center cohorts for pancreatic cancer

Libo Wang, Zaoqu Liu, Ruopeng Liang, Weijie Wang, Rongtao Zhu, Jian Li, Zhe Xing, Siyuan Weng, Xinwei Han, Yuling Sun

The paper develops an AI-derived prognostic signature for pancreatic cancer using multi-center cohorts and machine learning, showing improved predictive performance and clinical relevance.

eLife · Oct 17, 2022Peer-reviewedBioinformatics

Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer

Zaoqu Liu, Siyuan Weng, Qin Dang, Hui Xu, Yuqing Ren, Chunguang Guo, Zhe Xing, Zhenqiang Sun, Xinwei Han

The paper introduces a gene interaction perturbation network approach to classify colorectal cancer into six subtypes with distinct molecular and clinical features.

eLife · Sep 28, 2022Peer-reviewedGenetics