Combining machine learning and metabolomics to identify weight gain biomarkers

Combining machine learning and metabolomics to identify weight gain biomarkers

Citation Author(s):
Flavia L
Dias-Audibert
UNICAMP
Luiz C
Navarro
UNICAMP
Diogo N
Oliveira
UNICAMP
Jeany
Delafiori
UNICAMP
Carlos F O R
Melo
UNICAMP
Tatiane M
Guerreiro
UNICAMP
Mohamed Z
Dabaja
UNICAMP
Flavia T
Rosa
UniFil
Diego L
Petenuci
UEL
Maria A E
Watanabe
UEL
Licio A
Velloso
UNICAMP
Anderson R
Rocha
UNICAMP
Rodrigo R
Catharino
UNICAMP
Submitted by:
Luiz Claudio Navarro
Last updated:
Tue, 02/11/2020 - 11:39
DOI:
10.21227/k446-fp12
Data Format:
License:
Dataset Views:
532
Rating:
0
0 ratings - Please login to submit your rating.
Share / Embed Cite

This dataset was used in the article "Dias-Audibert FL, Navarro LC, de Oliveira DN, Delafiori J, Melo CFOR, Guerreiro TM, Rosa FT, Petenuci DL, Watanabe MAE, Velloso LA, Rocha AR and Catharino RR (2020) Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. Front. Bioeng. Biotechnol. 8:6. doi: 10.3389/fbioe.2020.00006", open access available at: https://doi.org/10.3389/fbioe.2020.00006.

Weight gain is a metabolic disorder that often culminates in the development of obesity and other comorbidities such as diabetes. Obesity is characterized by the development of a chronic, subclinical systemic inflammation, and is regarded as a remarkably important factor that contributes to the  development of such comorbidities. Therefore, laboratory methods that allow the identification of subjects at higher risk for severe weight-associated morbidity are of utter importance, considering the health and safety of populations. This contribution analyzed the plasma of 180 Brazilian individuals, equally divided into a eutrophic control group and case group, to assess the presence of biomarkers related to weight gain, aiming at characterizing the phenotype of this population. Samples were analyzed by mass spectrometry and most discriminant features were determined by a machine learning approach using Random Forest algorithm. Five biomarkers related to the pathogenesis and chronicity of inflammation in weight gain were identified. Two metabolites of arachidonic acid were upregulated in the case group, indicating the presence of inflammation, as well as two other molecules related to dysfunctions in the cycle of nitric oxide (NO) and increase in superoxide production. Finally, a fifth case group marker observed in this study may indicate the trigger for diabetes in overweight and obesity individuals. The use of mass spectrometry combined withmachine learning analyses to prospect and characterize biomarkers associated with weight gain will  pave the way for elucidating potential therapeutic and prognostic targets.

Instructions: 

WGMSML-Data folder contains the mass spectra input data for the Matlab scripts which are in WGMSML-MATLAB-SourceCode folder. WGMSML-ExecutionLogsAndPlots contains logs and plots generated by the execution of the Matlab code over the input data. Main scripts are enumerated in the order of execution.

Dataset Files

You must login with an IEEE Account to access these files. IEEE Accounts are FREE.

Sign Up now or login.

Embed this dataset on another website

Copy and paste the HTML code below to embed your dataset:

Share via email or social media

Click the buttons below:

facebooktwittermailshare
[1] Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Jeany Delafiori, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino, "Combining machine learning and metabolomics to identify weight gain biomarkers", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/k446-fp12. Accessed: Feb. 28, 2020.
@data{k446-fp12-19,
doi = {10.21227/k446-fp12},
url = {http://dx.doi.org/10.21227/k446-fp12},
author = {Flavia L Dias-Audibert; Luiz C Navarro; Diogo N Oliveira; Jeany Delafiori; Carlos F O R Melo; Tatiane M Guerreiro; Mohamed Z Dabaja; Flavia T Rosa; Diego L Petenuci; Maria A E Watanabe; Licio A Velloso; Anderson R Rocha; Rodrigo R Catharino },
publisher = {IEEE Dataport},
title = {Combining machine learning and metabolomics to identify weight gain biomarkers},
year = {2019} }
TY - DATA
T1 - Combining machine learning and metabolomics to identify weight gain biomarkers
AU - Flavia L Dias-Audibert; Luiz C Navarro; Diogo N Oliveira; Jeany Delafiori; Carlos F O R Melo; Tatiane M Guerreiro; Mohamed Z Dabaja; Flavia T Rosa; Diego L Petenuci; Maria A E Watanabe; Licio A Velloso; Anderson R Rocha; Rodrigo R Catharino
PY - 2019
PB - IEEE Dataport
UR - 10.21227/k446-fp12
ER -
Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Jeany Delafiori, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino. (2019). Combining machine learning and metabolomics to identify weight gain biomarkers. IEEE Dataport. http://dx.doi.org/10.21227/k446-fp12
Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Jeany Delafiori, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino, 2019. Combining machine learning and metabolomics to identify weight gain biomarkers. Available at: http://dx.doi.org/10.21227/k446-fp12.
Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Jeany Delafiori, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino. (2019). "Combining machine learning and metabolomics to identify weight gain biomarkers." Web.
1. Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Jeany Delafiori, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino. Combining machine learning and metabolomics to identify weight gain biomarkers [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/k446-fp12
Flavia L Dias-Audibert, Luiz C Navarro, Diogo N Oliveira, Jeany Delafiori, Carlos F O R Melo, Tatiane M Guerreiro, Mohamed Z Dabaja, Flavia T Rosa, Diego L Petenuci, Maria A E Watanabe, Licio A Velloso, Anderson R Rocha, Rodrigo R Catharino. "Combining machine learning and metabolomics to identify weight gain biomarkers." doi: 10.21227/k446-fp12