Work

GDAS: A Machine Learning-Driven Approach for the High-Throughput Identification of Disease-Associated Glycoforms

Year: 2026

Type: article

Source: Analytical Chemistry

DOI: 10.1021/acs.analchem.6c01321

Authors: Shengye Wen, Yu Gao, Xinyu Miao, Jianbo Deng, Yufeng Zhou, Wei Ge, Siyue Bo, Wenqi Zhang, Rumeng Zhang, Chunyan Hou, Junfeng Ma, Junhong Jiang, Shuang Yang

Institutions: Soochow University, First Affiliated Hospital of Shantou University Medical College, Shantou University Medical College, The Fourth Affiliated Hospital of Soochow University (Suzhou Dushu Lake Hospital), Georgetown University, Georgetown University Medical Center, Georgetown Lombardi Comprehensive Cancer Center

Language: English

Topic: Glycosylation and Glycoproteins Research

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: Government of Jiangsu Province, Shantou University Medical College, Shuangchuang Program of Jiangsu Province

Awards: G2077227819, G7616405730

OpenAlex ID: W7161570485

FWCI: 1.939

Cites: 55

Cited by: 1

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