Retention Time Prediction Based Proteomic Analysis on Proteins from Glycolysis/Gluconeogenesis Pathway of E. coli

2012 ◽  
Author(s):  
Li Wang ◽  
Yongqian Zhang ◽  
Hong Qing ◽  
Yajun Wang ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Retention Time ◽  
Proteomic Analysis ◽  
E Coli ◽  
Retention Time Prediction ◽  
Time Prediction

scholarly journals HPLC Retention time prediction for metabolome analysis

2010 ◽  
Vol 5 (6) ◽  
pp. 255-258 ◽  
Author(s):  
Takashi Hagiwara ◽  
Seiji Saito ◽  
Yoshifumi Ujiie ◽  
Kensaku Imai ◽  
Masanori Kakuta ◽  
...  
Keyword(s):  
Retention Time ◽  
Metabolome Analysis ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Hplc Retention Time

scholarly journals The METLIN small molecule dataset for machine learning-based retention time prediction

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Xavier Domingo-Almenara ◽  
Carlos Guijas ◽  
Elizabeth Billings ◽  
J. Rafael Montenegro-Burke ◽  
Winnie Uritboonthai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecule ◽  
Retention Time ◽  
Chromatographic Retention ◽  
Retention Time Prediction ◽  
Molecular Identity ◽  
Time Prediction ◽  
Wide Range ◽  
Small Molecule Analysis ◽  
Deep Learning Model

AbstractMachine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70$$\%$$% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

Chromatographic retention time prediction for posttranslationally modified peptides

PROTEOMICS ◽  
2012 ◽  
Vol 12 (8) ◽  
pp. 1151-1159 ◽  
Author(s):  
Luminita Moruz ◽  
An Staes ◽  
Joseph M. Foster ◽  
Maria Hatzou ◽  
Evy Timmerman ◽  
...  
Keyword(s):  
Retention Time ◽  
Chromatographic Retention ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Modified Peptides

Enhanced metabolite annotation via dynamic retention time prediction: Steroidogenesis alterations as a case study

2017 ◽  
Vol 1071 ◽  
pp. 11-18 ◽  
Author(s):  
Giuseppe Marco Randazzo ◽  
David Tonoli ◽  
Petra Strajhar ◽  
Ioannis Xenarios ◽  
Alex Odermatt ◽  
...  
Keyword(s):  
Retention Time ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Metabolite Annotation

Liquid Chromatography at Critical Conditions:  Comprehensive Approach to Sequence-Dependent Retention Time Prediction

2006 ◽  
Vol 78 (22) ◽  
pp. 7770-7777 ◽  
Author(s):  
Alexander V. Gorshkov ◽  
Irina A. Tarasova ◽  
Victor V. Evreinov ◽  
Mikhail M. Savitski ◽  
Michael L. Nielsen ◽  
...  
Keyword(s):  
Liquid Chromatography ◽  
Retention Time ◽  
Comprehensive Approach ◽  
Critical Conditions ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Sequence Dependent
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