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45 results on '"Li, Mansheng"'

4. Large‐Scale Proteome Profiling Identifies Biomarkers Associated with Suspected Neurosyphilis Diagnosis (Adv. Sci. 16/2024)

Author

Li, Jun, primary, Ma, Jie, additional, Liu, MingJuan, additional, Li, Mansheng, additional, Zhang, Ming, additional, Yin, Wenhao, additional, Wu, Mengyin, additional, Li, Xiao, additional, Zhang, Qiyu, additional, Zhang, Hanlin, additional, Zheng, Heyi, additional, Mao, Chenhui, additional, Sun, Jian, additional, Wang, Wenze, additional, Lyu, Wei, additional, Yue, Xueping, additional, Weng, Wenjia, additional, Li, Juan, additional, Chen, Fengxin, additional, Zhu, Yunping, additional, and Leng, Ling, additional

Published
2024
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7. Dynamic atlas of immune cells reveals multiple functional features of macrophages associated with progression of pulmonary fibrosis

Author

Lv, Jiaoyan, primary, Gao, Haoxiang, additional, Ma, Jie, additional, Liu, Jiachen, additional, Tian, Yujie, additional, Yang, Chunyuan, additional, Li, Mansheng, additional, Zhao, Yue, additional, Li, Zhimin, additional, Zhang, Xuegong, additional, Zhu, Yunping, additional, Zhang, Jianhong, additional, and Wu, Li, additional

Published
2023
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9. Study of Soil Water Retention in Horqin Sand Land

Author

Yao, Shuxia, Zhao, Chuancheng, Wang, Suyun, Liu, Yongli, Li, Mansheng, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bian, Fuling, editor, and Xie, Yichun, editor

Published
2016
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10. Additional file 6 of Integrated analysis of circulating and tissue proteomes reveals that fibronectin 1 is a potential biomarker in papillary thyroid cancer

Author

Ye, Guochao, Zhang, Xiaomei, Li, Mansheng, Lin, Zixiang, Xu, Yongcan, Dong, Haoru, Zhou, Jie, Zhang, Jiaqi, Wang, Sheng, Zhu, Yunping, Yu, Xiaobo, and Qian, Xu

Abstract

Additional file 6: Figure S1. Quality control of antibody microarray and DIA-MS based proteomics. Figure S2. Machine learning‐based classification of Normal and Tumor group. Figure S3. (A) Differential serum expressions of FN1 and GSN levels were validated between patients with benign nodules and papillary thyroid cancer (PTC) by ELISA tests in an in-dependent cohort. (B) Receiver operatingcharacteristic (ROC) curve for FN1, GSN based on the result of ELISA tests.

Published
2023
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12. Application of an iPSC‐Derived Organoid Model for Localized Scleroderma Therapy (Adv. Sci. 16/2022)

Author

Ma, Jie, primary, Li, Wei, additional, Cao, Ruiyuan, additional, Gao, Dunqin, additional, Zhang, Qiyu, additional, Li, Xiao, additional, Li, Biyou, additional, Lv, Luye, additional, Li, Mansheng, additional, Jiang, Junyi, additional, Wang, Yujie, additional, Li, Jun, additional, Wu, Zhihong, additional, Zhu, Yunping, additional, Zhong, Wu, additional, Zhang, Shuyang, additional, and Leng, Ling, additional

Published
2022
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16. iProX in 2021: connecting proteomics data sharing with big data

Author

Chen, Tao, primary, Ma, Jie, additional, Liu, Yi, additional, Chen, Zhiguang, additional, Xiao, Nong, additional, Lu, Yutong, additional, Fu, Yinjin, additional, Yang, Chunyuan, additional, Li, Mansheng, additional, Wu, Songfeng, additional, Wang, Xue, additional, Li, Dongsheng, additional, He, Fuchu, additional, Hermjakob, Henning, additional, and Zhu, Yunping, additional

Published
2021
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17. Time-Resolved Extracellular Matrix Atlas of the Developing Human Skin Dermis

Author

Li, Mansheng, primary, Li, Xiao, additional, Liu, Binghui, additional, Lv, Luye, additional, Wang, Wenjuan, additional, Gao, Dunqin, additional, Zhang, Qiyu, additional, Jiang, Junyi, additional, Chai, Mi, additional, Yun, Zhimin, additional, Tan, Yingxia, additional, Gong, Feng, additional, Wu, Zhihong, additional, Zhu, Yunping, additional, Ma, Jie, additional, and Leng, Ling, additional

Published
2021
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20. Additional file 5 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Author

Li, Mansheng, He, Qiang, Yang, Chunyuan, Ma, Jie, He, Fuchu, Chen, Tao, and Zhu, Yunping

Subjects
ComputingMethodologies_PATTERNRECOGNITION, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, ComputingMethodologies_ARTIFICIALINTELLIGENCE
Abstract

Table S5. Verbs and nouns used to construct Interaction Type sub-ontology.

Published
2021
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30. Construction, Assessment and Applications of Biomedical Ontologies

Author

Yang ChunYuan, Zhu Yunping, and Li Mansheng

Subjects
Open Biomedical Ontologies, Computer science, Process ontology, Ontology-based data integration, Suggested Upper Merged Ontology, Upper ontology, Pharmacology (medical), Ontology (information science), computer.software_genre, Data science, Ontology alignment, computer, Data integration
Abstract

This review first introduces the concept and essential characteristics of ontology, and presents the general construction and evaluation methodology of domain ontology. Several examples illustrate the applications of biomedical ontologies in biological data annotation, enrichment analysis, data integration, database and library construction, text mining and other aspects. The current commonly used biomedical ontology databases, ontology description languages and editing softwares are also summarized. Finally, we discuss some common problems in the establishment and application of biomedical ontologies, and provide a few suggestions for future developments of this filed.

Published
2013

38. PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis.

Author

Chang, Cheng, Li, Mansheng, Guo, Chaoping, Ding, Yuqing, Xu, Kaikun, Han, Mingfei, He, Fuchu, and Zhu, Yunping

Subjects
*PROTEOMICS, *GIANT panda, *MASS spectrometry, *BIG data, *PEPTIDES
Abstract

Summary As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces. Availability and implementation PANDA is freely available for download at https://sourceforge.net/projects/panda-tools/. Supplementary information Supplementary data are available at Bioinformatics online [ABSTRACT FROM AUTHOR]

Published
2019
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39. A Proteomics Strategy for the Identification of FAT10-Modified Sites by Mass Spectrometry

Author

Leng, Ling, primary, Xu, Changming, additional, Wei, Chao, additional, Zhang, Jiyang, additional, Liu, Boya, additional, Ma, Jie, additional, Li, Ning, additional, Qin, Weijie, additional, Zhang, Wanjun, additional, Zhang, Chengpu, additional, Xing, Xiaohua, additional, Zhai, Linhui, additional, Yang, Fan, additional, Li, Mansheng, additional, Jin, Chaozhi, additional, Yuan, Yanzhi, additional, Xu, Ping, additional, Qin, Jun, additional, Xie, Hongwei, additional, He, Fuchu, additional, and Wang, Jian, additional

Published
2013
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43. A Proteomics Strategy for the Identification of FAT10-Modified Sites by Mass Spectrometry

Author

Leng, Ling, Xu, Changming, Wei, Chao, Zhang, Jiyang, Liu, Boya, Ma, Jie, Li, Ning, Qin, Weijie, Zhang, Wanjun, Zhang, Chengpu, Xing, Xiaohua, Zhai, Linhui, Yang, Fan, Li, Mansheng, Jin, Chaozhi, Yuan, Yanzhi, Xu, Ping, Qin, Jun, Xie, Hongwei, He, Fuchu, and Wang, Jian

Abstract

The ubiquitin-like protein FAT10 (HLA-F adjacent transcript 10) is uniquely expressed in mammals. The fat10gene is encoded in the MHC class I locus in the human genome and is related to some specific processes, such as apoptosis, immune response, and cancer. However, biological knowledge of FAT10 is limited, owing to the lack of identification of its conjugates. FAT10 covalently modifies proteins in eukaryotes, but only a few substrates of FAT10 have been reported until now, and no FATylated sites have been identified. Here, we report the proteome-scale identification of FATylated proteins by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We identified 175 proteins with high confidence as FATylated candidates. A total of 13 modified sites were identified for the first time by a modified search of the raw MS data. The modified sites were highly enriched with hydrophilic amino acids. Furthermore, the FATylation processes of hnRNP C2, PCNA, and PDIA3 were verified by a coimmunoprecipitation assay. We confirmed that most of the substrates were covalently attached to a FAT10 monomer. The functional distribution of the FAT10 targets suggests that FAT10 participates in various biological processes, such as translation, protein folding, RNA processing, and macromolecular complex assembly. These results should be very useful for investigating the biological functions of FAT10.

Published
2014
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44. SARS-CoV-2 Evolution: Immune Dynamics, Omicron Specificity, and Predictive Modeling in Vaccinated Populations.

Author

Zhang X, Li M, Zhang N, Li Y, Teng F, Li Y, Zhang X, Xu X, Li H, Zhu Y, Wang Y, Jia Y, Qin C, Wang B, Guo S, Wang Y, and Yu X

Abstract

Host immunity is central to the virus's spread dynamics, which is significantly influenced by vaccination and prior infection experiences. In this work, we analyzed the co-evolution of SARS-CoV-2 mutation, angiotensin-converting enzyme 2 (ACE2) receptor binding, and neutralizing antibody (NAb) responses across various variants in 822 human and mice vaccinated with different non-Omicron and Omicron vaccines is analyzed. The link between vaccine efficacy and vaccine type, dosing, and post-vaccination duration is revealed. The classification of immune protection against non-Omicron and Omicron variants is co-evolved with genetic mutations and vaccination. Additionally, a model, the Prevalence Score (P-Score) is introduced, which surpasses previous algorithm-based models in predicting the potential prevalence of new variants in vaccinated populations. The hybrid vaccination combining the wild-type (WT) inactivated vaccine with the Omicron BA.4/5 mRNA vaccine may provide broad protection against both non-Omicron variants and Omicron variants, albeit with EG.5.1 still posing a risk. In conclusion, these findings enhance understanding of population immunity variations and provide valuable insights for future vaccine development and public health strategies., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Published
2024
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45. iProX in 2021: connecting proteomics data sharing with big data.

Author

Chen T, Ma J, Liu Y, Chen Z, Xiao N, Lu Y, Fu Y, Yang C, Li M, Wu S, Wang X, Li D, He F, Hermjakob H, and Zhu Y

Subjects
Big Data, Computational Biology standards, Information Dissemination, Databases, Protein, Proteome genetics, Proteomics, Software
Abstract

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Published
2022
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