PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only-Reference-Cited by-同舟云学术

PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only

Published:2021-04-12 Issue:16 Volume:93 Page:6481-6490
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Cheng Hao¹²,Rao Bing³,Liu Lei¹²,Cui Lizhen¹²,Xiao Guobao⁴,Su Ran⁵,Wei Leyi¹²⁴^ORCID

Affiliation:

1. School of Software, Shandong University, Jinan 250101, China

2. Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China

3. School of Mechanical Electronic & Information Engineering, China University of Mining &Technology, Beijing 221008, China

4. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou 350000, China

5. College of Intelligence and Computing, Tianjin University, Tianjin 300384, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.1c00354

Reference23 articles.

1. Mass spectrometry-based proteomics

2. Human Liver Proteome Project

3. Analysis of Intrinsic Peptide Detectability via Integrated Label-Free and SRM-Based Absolute Quantitative Proteomics

4. A computational approach toward label-free protein quantification using predicted peptide detectability

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