Prediction of Liquid Chromatographic Retention Times of Peptides Generated by Protease Digestion of the Escherichia coli Proteome Using Artificial Neural Networks-Reference-Cited by-同舟云学术

Prediction of Liquid Chromatographic Retention Times of Peptides Generated by Protease Digestion of the Escherichia coli Proteome Using Artificial Neural Networks

Published:2006-11-02 Issue:12 Volume:5 Page:3312-3317
ISSN:1535-3893
Container-title:Journal of Proteome Research
language:en
Short-container-title:J. Proteome Res.

Author:

Shinoda Kosaku¹,Sugimoto Masahiro¹,Yachie Nozomu¹,Sugiyama Naoyuki¹,Masuda Takeshi¹,Robert Martin¹,Soga Tomoyoshi¹,Tomita Masaru¹

Affiliation:

1. Human Metabolome Technologies, Inc., Tsuruoka, Yamagata 997-0052, Japan, Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017 Japan, and Bioinformatics Department, Mitsubishi Space Software Company Ltd., Amagasaki, Hyogo, 661-0001, Japan

Publisher

American Chemical Society (ACS)

Subject

General Chemistry,Biochemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/pr0602038

Reference31 articles.

1. Prediction of Chromatographic Retention and Protein Identification in Liquid Chromatography/Mass Spectrometry

2. Prediction of Peptide Retention at Different HPLC Conditions from Multiple Linear Regression Models

3. Prediction of high-performance liquid chromatography retention of peptides with the use of quantitative structure-retention relationships

Cited by 51 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improved LC–MS identification of short homologous peptides using sequence-specific retention time predictors;Analytical and Bioanalytical Chemistry;2023-03-31

2. Exploring the variable space of shallow machine learning models for reversed-phase retention time prediction;Computational and Structural Biotechnology Journal;2023

3. Amalgamation of comparative protein modeling with quantitative structure-retention relationship for prediction of the chromatographic behavior of peptides;Journal of Chromatography A;2022-04

4. Deep learning approaches for data-independent acquisition proteomics;Expert Review of Proteomics;2021-12-02

5. Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia;Journal of Biomedical Informatics;2021-10