Summary of Study ST001271

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000857. The data can be accessed directly via it's Project DOI: 10.21228/M8X110 This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST001271
Study TitleColorectal cancer before and after surgery metabolomics data integration
Study TypePilot study
Study SummaryIn this study 40 urine samples were analyzed in 5 batches. There are 3 labels: control group volunteers (8 pcs., CG), colorectal cancer patients before surgical operation (20 pcs., pre) and after surgical operation (12 pcs., post).
Institute
Lomonosov MSU
DepartmentChemistry
LaboratoryMass spectrometry
Last NamePlyushchenko
First NameIvan
AddressLeninskie gory, 1/3
Emailplyush1993@bk.ru
Phone89160362200
Submit Date2019-10-29
Analysis Type DetailLC-MS
Release Date2020-01-13
Release Version1
Ivan Plyushchenko Ivan Plyushchenko
https://dx.doi.org/10.21228/M8X110
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR000857
Project DOI:doi: 10.21228/M8X110
Project Title:Colorectal cancer before and after surgery metabolomics data integration
Project Type:Pilot study
Project Summary:In this study 40 urine samples were analyzed in 5 batches. There are 3 labels: control group volunteers (8 pcs., CG), colorectal cancer patients before surgical operation (20 pcs., pre) and after surgical operation (12 pcs., post).
Institute:Lomonosov Moscow State University
Department:Chemistry
Laboratory:Mass spectrometry
Last Name:Plyushchenko
First Name:Ivan
Address:Leninskie gory, 1/3, Moscow, Moscow, 119234, Russian Federation
Email:plyush1993@bk.ru
Phone:+79160362200
Publications:Plyushchenko Ivan, et al. "An approach for feature selection with data modelling in LC-MS metabolomics." Analytical Methods 12.28 (2020): 3582-3591. DOI: 10.1039/d0ay00204f
Contributors:Rodin I., Shakhmatov D., Bolotnik T.
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