Potential of deep representative learning features to interpret the sequence information in proteomics

Research output: Contribution to journal › Comment/debate › peer-review

ASJC Scopus subject areas

@article{ba7cff79b19d4d05af60f49e3667d734,

title = "Potential of deep representative learning features to interpret the sequence information in proteomics",

author = "Le, {Nguyen Quoc Khanh}",

note = "Funding Information: This work was supported by the Ministry of Science and Technology, Taiwan. [Grant number MOST110‐2221‐E‐038‐001‐MY2] ",

year = "2022",

month = jan,

doi = "10.1002/pmic.202100232",

language = "English",

volume = "22",

journal = "Proteomics",

issn = "1615-9853",

publisher = "Wiley-VCH Verlag",

number = "1-2",

}

TY - JOUR

T1 - Potential of deep representative learning features to interpret the sequence information in proteomics

AU - Le, Nguyen Quoc Khanh

N1 - Funding Information: This work was supported by the Ministry of Science and Technology, Taiwan. [Grant number MOST110‐2221‐E‐038‐001‐MY2]

PY - 2022/1

Y1 - 2022/1

UR - http://www.scopus.com/inward/record.url?scp=85123299292&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85123299292&partnerID=8YFLogxK

U2 - 10.1002/pmic.202100232

DO - 10.1002/pmic.202100232

M3 - Comment/debate

C2 - 34730875

AN - SCOPUS:85123299292

SN - 1615-9853

VL - 22

JO - Proteomics

JF - Proteomics

IS - 1-2

M1 - 2100232

ER -