Random Forest analysis of cohort 2 sample data using 25 metabolites selected from cohort.

Random forest analysis showing discriminating biomarkers of diet-challenge in their decreasing orders of mean decrease accuracies (MDA).

Biswapriya B. Misra (563793)
Sobha R. Puppala (6560249)
Anthony G. Comuzzie (43835)
Michael C. Mahaney (387814)
John L. VandeBerg (195842)
Michael Olivier (200267)
Laura A. Cox (406154)

April 2019

Smaller MDAs tend to be better discriminatory markers than the higher values.</p

Random forest analysis of the thirty most important metabolites distinguishing patients with severe acute AAH from stable, alcoholic cirrhotic controls.

Vikrant Rachakonda (655827)
Charles Gabbert (668190)
Amit Raina (668191)
Lauren N. Bell (502768)
Sara Cooper (668192)
Shahid Malik (668193)
Jaideep Behari (286547)

December 2014

Random forest analysis of the thirty most important metabolites distinguishing patients with seve...

Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection

Tianlu Chen
Yu Cao
Yinan Zhang
Jiajian Liu
Yuqian Bao
Congrong Wang
Weiping Jia
Aihua Zhao

January 2013

Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with d...

Metabolite ratio importance plot.

Gabriele Anton (714792)
Rory Wilson (332489)
Zhong-hao Yu (5662840)
Cornelia Prehn (137977)
Sven Zukunft (697634)
Jerzy Adamski (127482)
Margit Heier (200535)
Christa Meisinger (39940)
Werner Römisch-Margl (127480)
Rui Wang-Sattler (36841)
Kristian Hveem (189996)
Bruce Wolfenbuttel (714793)
Annette Peters (62601)
Gabi Kastenmüller (127487)
Melanie Waldenberger (236985)

March 2015

The importance for each metabolite ratio in classifying samples into ‘good’ or ‘bad’ pre-storage ...

Random forest analysis of the plasma metabolome.

Satoshi Kume (534079)
Masanori Yamato (534076)
Yasuhisa Tamura (534077)
Guanghua Jin (707133)
Masayuki Nakano (480677)
Yukiharu Miyashige (534080)
Asami Eguchi (534078)
Yoshiyuki Ogata (333782)
Nobuhito Goda (707134)
Kazuhiro Iwai (217191)
Emi Yamano (707135)
Yasuyoshi Watanabe (15245)
Tomoyoshi Soga (80283)
Yosky Kataoka (534081)

March 2015

The mean decrease in accuracy from the random forest analysis was used to rank metabolites accord...

Top 30 Metabolites Based on their Importance for Separating the Groups (Affected and Unaffected Tissues).

Behnam Abasht (785740)
Marie F. Mutryn (2620879)
Ryan D. Michalek (483950)
William R. Lee (785739)

April 2016

Random forest analysis (RFA) was utilized to determine which variables (biochemicals) had the lar...

Selection of metabolites for joint analysis based on their ranking in top 30 metabolites in correlation analysis, metabolite significance of weighted network analysis and variable importance of random forest regression.

Julia Welzenbach (2504284)
Christiane Neuhoff (412997)
Christian Looft (413001)
Karl Schellander (79383)
Ernst Tholen (79379)
Christine Große-Brinkhaus (412998)

February 2016

Selection of metabolites for joint analysis based on their ranking in top 30 metabolites in corre...

Random survival forest in practice: A method for modelling complex metabolomics data in time to event analysis.

Dietrich, S.
Floegel, A.
Troll, M.
Kuhn, T.
Rathmann, W.
Peters, A.
Sookthai, D.
von Bergen, M.
Kaaks, R.
Adamski, J.
Prehn, C.
Boeing, H.
Schulze, M.B.
Illig, T.
Pischon, T.
Knüppel, S.
Wang-Sattler, R.
Drogan, D.

January 2016

BACKGROUND: The application of metabolomics in prospective cohort studies is statistically challengi...

Using Random Forest in the field of metabolomics

White, Curtis

November 2014

Metabolomics is the science of comprehensive evaluation of changes in the metabolome with a goal to ...

Random forest analysis.

Milena Schmidt (5634908)
Stefan Unterer (107353)
Jan S. Suchodolski (107343)
Julia B. Honneffer (5634911)
Blake C. Guard (743494)
Jonathan A. Lidbury (5634914)
Jörg M. Steiner (107366)
Julia Fritz (3313950)
Petra Kölle (5634917)

August 2018

Top 15 metabolites with the highest discriminatory power between both diet groups are listed. Red fi...

Principal metabolic differences between Sc growing at 12°C and at 28°C.

María López-Malo (392289)
Amparo Querol (392290)
José Manuel Guillamon (392291)

March 2013

A) Class distribution of the different identified metabolites. n = number of metabolites in eac...

Random forest classification.

Manpreet Randhawa (531389)
Vineet Sangar (34508)
Samantha Tucker-Samaras (531390)
Michael Southall (531391)

March 2014

Random forest classification presenting the prioritized list of 30 metabolites. Metabolites with ...

Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals

Enot, David P.
Beckmann, Manfred
Overy, David Patrick
Draper, John

October 2006

Powerful algorithms are required to deal with the dimensionality of metabolomics data. Although many...

Top 20 replicated metabolites, logistic regression.

Angela J. Rogers (504190)
Michael McGeachie (517221)
Rebecca M. Baron (364782)
Lee Gazourian (504191)
Jeffrey A. Haspel (315475)
Kiichi Nakahira (270241)
Laura E. Fredenburgh (504199)
Gary M. Hunninghake (94903)
Benjamin A. Raby (94904)
Michael A. Matthay (266536)
Ronny M. Otero (104183)
Vance G. Fowler (517222)
Emanuel P. Rivers (104181)
Christopher W. Woods (104194)
Stephen Kingsmore (208246)
Ray J. Langley (376061)
Augustine M. K. Choi (39903)

January 2014

β and p values shown are for results for association with 28-day mortality, using logistic regres...

Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals

Enot, David P.
Beckmann, Manfred
Overy, David Patrick
Draper, John

October 2006

Powerful algorithms are required to deal with the dimensionality of metabolomics data. Although many...

Random forest analysis showing discriminating biomarkers of diet-challenge in their decreasing orders of mean decrease accuracies (MDA).

Biswapriya B. Misra (563793)
Sobha R. Puppala (6560249)
Anthony G. Comuzzie (43835)
Michael C. Mahaney (387814)
John L. VandeBerg (195842)
Michael Olivier (200267)
Laura A. Cox (406154)

April 2019

Smaller MDAs tend to be better discriminatory markers than the higher values.</p

Random forest analysis of the thirty most important metabolites distinguishing patients with severe acute AAH from stable, alcoholic cirrhotic controls.

Vikrant Rachakonda (655827)
Charles Gabbert (668190)
Amit Raina (668191)
Lauren N. Bell (502768)
Sara Cooper (668192)
Shahid Malik (668193)
Jaideep Behari (286547)

December 2014

Random forest analysis of the thirty most important metabolites distinguishing patients with seve...

Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection

Tianlu Chen
Yu Cao
Yinan Zhang
Jiajian Liu
Yuqian Bao
Congrong Wang
Weiping Jia
Aihua Zhao

January 2013

Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with d...

Metabolite ratio importance plot.

Gabriele Anton (714792)
Rory Wilson (332489)
Zhong-hao Yu (5662840)
Cornelia Prehn (137977)
Sven Zukunft (697634)
Jerzy Adamski (127482)
Margit Heier (200535)
Christa Meisinger (39940)
Werner Römisch-Margl (127480)
Rui Wang-Sattler (36841)
Kristian Hveem (189996)
Bruce Wolfenbuttel (714793)
Annette Peters (62601)
Gabi Kastenmüller (127487)
Melanie Waldenberger (236985)

March 2015

The importance for each metabolite ratio in classifying samples into ‘good’ or ‘bad’ pre-storage ...

Random forest analysis of the plasma metabolome.

Satoshi Kume (534079)
Masanori Yamato (534076)
Yasuhisa Tamura (534077)
Guanghua Jin (707133)
Masayuki Nakano (480677)
Yukiharu Miyashige (534080)
Asami Eguchi (534078)
Yoshiyuki Ogata (333782)
Nobuhito Goda (707134)
Kazuhiro Iwai (217191)
Emi Yamano (707135)
Yasuyoshi Watanabe (15245)
Tomoyoshi Soga (80283)
Yosky Kataoka (534081)

March 2015

The mean decrease in accuracy from the random forest analysis was used to rank metabolites accord...

Top 30 Metabolites Based on their Importance for Separating the Groups (Affected and Unaffected Tissues).

Behnam Abasht (785740)
Marie F. Mutryn (2620879)
Ryan D. Michalek (483950)
William R. Lee (785739)

April 2016

Random forest analysis (RFA) was utilized to determine which variables (biochemicals) had the lar...

Selection of metabolites for joint analysis based on their ranking in top 30 metabolites in correlation analysis, metabolite significance of weighted network analysis and variable importance of random forest regression.

Julia Welzenbach (2504284)
Christiane Neuhoff (412997)
Christian Looft (413001)
Karl Schellander (79383)
Ernst Tholen (79379)
Christine Große-Brinkhaus (412998)

February 2016

Selection of metabolites for joint analysis based on their ranking in top 30 metabolites in corre...

Random survival forest in practice: A method for modelling complex metabolomics data in time to event analysis.

Dietrich, S.
Floegel, A.
Troll, M.
Kuhn, T.
Rathmann, W.
Peters, A.
Sookthai, D.
von Bergen, M.
Kaaks, R.
Adamski, J.
Prehn, C.
Boeing, H.
Schulze, M.B.
Illig, T.
Pischon, T.
Knüppel, S.
Wang-Sattler, R.
Drogan, D.

January 2016

BACKGROUND: The application of metabolomics in prospective cohort studies is statistically challengi...

Using Random Forest in the field of metabolomics

White, Curtis

November 2014

Metabolomics is the science of comprehensive evaluation of changes in the metabolome with a goal to ...

Random forest analysis.

Milena Schmidt (5634908)
Stefan Unterer (107353)
Jan S. Suchodolski (107343)
Julia B. Honneffer (5634911)
Blake C. Guard (743494)
Jonathan A. Lidbury (5634914)
Jörg M. Steiner (107366)
Julia Fritz (3313950)
Petra Kölle (5634917)

August 2018

Top 15 metabolites with the highest discriminatory power between both diet groups are listed. Red fi...

Principal metabolic differences between Sc growing at 12°C and at 28°C.

María López-Malo (392289)
Amparo Querol (392290)
José Manuel Guillamon (392291)

March 2013

A) Class distribution of the different identified metabolites. n = number of metabolites in eac...

Random forest classification.

Manpreet Randhawa (531389)
Vineet Sangar (34508)
Samantha Tucker-Samaras (531390)
Michael Southall (531391)

March 2014

Random forest classification presenting the prioritized list of 30 metabolites. Metabolites with ...

Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals

Enot, David P.
Beckmann, Manfred
Overy, David Patrick
Draper, John

October 2006

Powerful algorithms are required to deal with the dimensionality of metabolomics data. Although many...

Top 20 replicated metabolites, logistic regression.

Angela J. Rogers (504190)
Michael McGeachie (517221)
Rebecca M. Baron (364782)
Lee Gazourian (504191)
Jeffrey A. Haspel (315475)
Kiichi Nakahira (270241)
Laura E. Fredenburgh (504199)
Gary M. Hunninghake (94903)
Benjamin A. Raby (94904)
Michael A. Matthay (266536)
Ronny M. Otero (104183)
Vance G. Fowler (517222)
Emanuel P. Rivers (104181)
Christopher W. Woods (104194)
Stephen Kingsmore (208246)
Ray J. Langley (376061)
Augustine M. K. Choi (39903)

January 2014

β and p values shown are for results for association with 28-day mortality, using logistic regres...

Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals

Enot, David P.
Beckmann, Manfred
Overy, David Patrick
Draper, John

October 2006

Powerful algorithms are required to deal with the dimensionality of metabolomics data. Although many...

Random forest analysis showing discriminating biomarkers of diet-challenge in their decreasing orders of mean decrease accuracies (MDA).

Biswapriya B. Misra (563793)
Sobha R. Puppala (6560249)
Anthony G. Comuzzie (43835)
Michael C. Mahaney (387814)
John L. VandeBerg (195842)
Michael Olivier (200267)
Laura A. Cox (406154)

April 2019

Smaller MDAs tend to be better discriminatory markers than the higher values.</p

Random forest analysis of the thirty most important metabolites distinguishing patients with severe acute AAH from stable, alcoholic cirrhotic controls.

Vikrant Rachakonda (655827)
Charles Gabbert (668190)
Amit Raina (668191)
Lauren N. Bell (502768)
Sara Cooper (668192)
Shahid Malik (668193)
Jaideep Behari (286547)

December 2014

Random forest analysis of the thirty most important metabolites distinguishing patients with seve...

Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection

Tianlu Chen
Yu Cao
Yinan Zhang
Jiajian Liu
Yuqian Bao
Congrong Wang
Weiping Jia
Aihua Zhao

January 2013

Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with d...

Random Forest analysis of cohort 2 sample data using 25 metabolites selected from cohort.

Abstract

Extracted data

Random Forest analysis of cohort 2 sample data using 25 metabolites selected from cohort.

Abstract

Extracted data

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