Classification boxplots for the best-performing models and the Super Learner in classifying the IC50 censored outcome.

Performance of NOP models trained using SyNet compared to a shuffled version of this network (i.e. the same genes are present but randomly connected while keeping their degree intact).

Amin Allahyar (2620357)
Joske Ubels (6307664)
Jeroen de Ridder (66819)

February 2019

a. Bars indicate average performance of models across repeats and error bars denote the correspondin...

The effect of Combat harmonization in the external validation dataset on model performance per machine learning classifier for lymph node involvement (LNI), extracapsular extension (ECE), and Gleason score (GS) prediction.

Wietske I. Luining (17364125)
Daniela E. Oprea-Lager (9465294)
André N. Vis (9465291)
Reindert J. A. van Moorselaar (17364128)
Remco J. J. Knol (17364131)
Maurits Wondergem (9465276)
Ronald Boellaard (300971)
Matthijs C. F. Cysouw (17364134)

November 2023

The effect of Combat harmonization in the external validation dataset on model performance per machi...

Comparing predictive performance of statistical learning models on Medical data

Biney, Francis

January 2020

This work investigates the predictive performance of 10 Machine learning models on three medical dat...

Performance of the Super Learner and the top 5 individual models in classifying the IC50 censored outcome.

Craig A. Magaret (111759)
David C. Benkeser (6533012)
Brian D. Williamson (6533015)
Bhavesh R. Borate (6533018)
Lindsay N. Carpp (298877)
Ivelin S. Georgiev (688784)
Ian Setliff (6533021)
Adam S. Dingens (3294717)
Noah Simon (685914)
Marco Carone (251182)
Christopher Simpkins (6533024)
David Montefiori (161606)
Galit Alter (91383)
Wen-Han Yu (234340)
Michal Juraska (688780)
Paul T. Edlefsen (650757)
Shelly Karuna (3094875)
Nyaradzo M. Mgodi (3331383)
Srilatha Edugupanti (6533027)
Peter B. Gilbert (30581)

April 2019

Cross-validated AUC point estimates and 95% confidence intervals are shown for A) models trained on ...

Cross-validated receiver operating characteristic curves for the best-performing models in classifying the IC50 censored outcome.

Craig A. Magaret (111759)
David C. Benkeser (6533012)
Brian D. Williamson (6533015)
Bhavesh R. Borate (6533018)
Lindsay N. Carpp (298877)
Ivelin S. Georgiev (688784)
Ian Setliff (6533021)
Adam S. Dingens (3294717)
Noah Simon (685914)
Marco Carone (251182)
Christopher Simpkins (6533024)
David Montefiori (161606)
Galit Alter (91383)
Wen-Han Yu (234340)
Michal Juraska (688780)
Paul T. Edlefsen (650757)
Shelly Karuna (3094875)
Nyaradzo M. Mgodi (3331383)
Srilatha Edugupanti (6533027)
Peter B. Gilbert (30581)

April 2019

Results are shown for the top three cross-validated models plus the cross-validated performance of t...

Performance of the super learner models when applied to the test data.

Hugh J. W. Sturrock (172061)
Katelyn Woolheater (5778449)
Adam F. Bennett (5778452)
Ricardo Andrade-Pacheco (3408482)
Alemayehu Midekisa (4474249)

September 2018

Classifications were made using the country specific cutoffs at which CV-classification accuracy was...

Performance of the Super Learner and the top 5 individual models in predicting the quantitative log IC50 outcome.

Craig A. Magaret (111759)
David C. Benkeser (6533012)
Brian D. Williamson (6533015)
Bhavesh R. Borate (6533018)
Lindsay N. Carpp (298877)
Ivelin S. Georgiev (688784)
Ian Setliff (6533021)
Adam S. Dingens (3294717)
Noah Simon (685914)
Marco Carone (251182)
Christopher Simpkins (6533024)
David Montefiori (161606)
Galit Alter (91383)
Wen-Han Yu (234340)
Michal Juraska (688780)
Paul T. Edlefsen (650757)
Shelly Karuna (3094875)
Nyaradzo M. Mgodi (3331383)
Srilatha Edugupanti (6533027)
Peter B. Gilbert (30581)

April 2019

Cross-validated R2 point estimates and 95% confidence intervals are shown for A) models trained on d...

Classification performance of the three observed approaches.

Gregor Stiglic (327658)
Petra Povalej Brzan (834962)
Nino Fijacko (834963)
Fei Wang (19534)
Boris Delibasic (834964)
Alexandros Kalousis (834965)
Zoran Obradovic (834966)

December 2015

Four sets of boxplots represent predictive performance measured in Area under the ROC curve (AUC)...

Box plots of features selected in 1000 (100%) training folds by the best performing classification model.

Mattias Nilsson Benfatto (3235731)
Gustaf Öqvist Seimyr (3235725)
Jan Ygge (3235728)
Tony Pansell (3235734)
Agneta Rydberg (3235722)
Christer Jacobson (3235737)

December 2016

The box plots show the distribution of values, normalized to range between 0 and 1, by feature an...

Boxplot showing the predictive performance measures for prediction models.

Marcio Luis Acencio (380824)
Luiz Augusto Bovolenta (477002)
Esther Camilo (477003)
Ney Lemke (92374)

October 2013

Boxplot showing the distribution of recall, precision and AUC values for 1000 prediction models g...

Summary of the classification results in cross-validation on the learning sets and on the independent test sets using the “All binary” or “Two-tier” method.

Nathalie Jeanray (682816)
Raphaël Marée (32623)
Benoist Pruvot (682817)
Olivier Stern (682818)
Pierre Geurts (29807)
Louis Wehenkel (32624)
Marc Muller (116959)

January 2015

For each phenotype (column 1), the optimal mode of classification (“BAGS” or “C”) (second column)...

Prediction accuracy of the supervised learning algorithms for the datasets of different sampling resolutions.

Jinxiang Xi (518144)
Weizhong Zhao (801902)

January 2019

(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...

Super Learner

Polley, Eric

January 2010

The super learner is a general loss-based learning method designed to find the optimal combination o...

Overall schema of datasets (boxes) and processes (arrows) that led to the analyses (red).

Naihui Zhou (5570783)
Zachary D. Siegel (5570786)
Scott Zarecor (5570789)
Nigel Lee (633607)
Darwin A. Campbell (74865)
Carson M. Andorf (335871)
Dan Nettleton (6085)
Carolyn J. Lawrence-Dill (4241194)
Baskar Ganapathysubramanian (633609)
Jonathan W. Kelly (5570792)
Iddo Friedberg (9867)

July 2018

Top row: The Expert Labeled dataset was used a gold standard to analyze how well the different ex...

Mean accuracy (MA) boxplots for the different co-training methods.

Weifeng Liu (635311)
Yang Li (7082)
Xu Lin (36043)
Dacheng Tao (331902)
Yanjiang Wang (635312)

September 2014

Each subfigure corresponds to one case of labeled examples. (A) MA obtained using 10% labeled exa...

Performance of NOP models trained using SyNet compared to a shuffled version of this network (i.e. the same genes are present but randomly connected while keeping their degree intact).

Amin Allahyar (2620357)
Joske Ubels (6307664)
Jeroen de Ridder (66819)

February 2019

a. Bars indicate average performance of models across repeats and error bars denote the correspondin...

The effect of Combat harmonization in the external validation dataset on model performance per machine learning classifier for lymph node involvement (LNI), extracapsular extension (ECE), and Gleason score (GS) prediction.

Wietske I. Luining (17364125)
Daniela E. Oprea-Lager (9465294)
André N. Vis (9465291)
Reindert J. A. van Moorselaar (17364128)
Remco J. J. Knol (17364131)
Maurits Wondergem (9465276)
Ronald Boellaard (300971)
Matthijs C. F. Cysouw (17364134)

November 2023

The effect of Combat harmonization in the external validation dataset on model performance per machi...

Comparing predictive performance of statistical learning models on Medical data

Biney, Francis

January 2020

This work investigates the predictive performance of 10 Machine learning models on three medical dat...

Performance of the Super Learner and the top 5 individual models in classifying the IC50 censored outcome.

Craig A. Magaret (111759)
David C. Benkeser (6533012)
Brian D. Williamson (6533015)
Bhavesh R. Borate (6533018)
Lindsay N. Carpp (298877)
Ivelin S. Georgiev (688784)
Ian Setliff (6533021)
Adam S. Dingens (3294717)
Noah Simon (685914)
Marco Carone (251182)
Christopher Simpkins (6533024)
David Montefiori (161606)
Galit Alter (91383)
Wen-Han Yu (234340)
Michal Juraska (688780)
Paul T. Edlefsen (650757)
Shelly Karuna (3094875)
Nyaradzo M. Mgodi (3331383)
Srilatha Edugupanti (6533027)
Peter B. Gilbert (30581)

April 2019

Cross-validated AUC point estimates and 95% confidence intervals are shown for A) models trained on ...

Cross-validated receiver operating characteristic curves for the best-performing models in classifying the IC50 censored outcome.

Craig A. Magaret (111759)
David C. Benkeser (6533012)
Brian D. Williamson (6533015)
Bhavesh R. Borate (6533018)
Lindsay N. Carpp (298877)
Ivelin S. Georgiev (688784)
Ian Setliff (6533021)
Adam S. Dingens (3294717)
Noah Simon (685914)
Marco Carone (251182)
Christopher Simpkins (6533024)
David Montefiori (161606)
Galit Alter (91383)
Wen-Han Yu (234340)
Michal Juraska (688780)
Paul T. Edlefsen (650757)
Shelly Karuna (3094875)
Nyaradzo M. Mgodi (3331383)
Srilatha Edugupanti (6533027)
Peter B. Gilbert (30581)

April 2019

Results are shown for the top three cross-validated models plus the cross-validated performance of t...

Performance of the super learner models when applied to the test data.

Hugh J. W. Sturrock (172061)
Katelyn Woolheater (5778449)
Adam F. Bennett (5778452)
Ricardo Andrade-Pacheco (3408482)
Alemayehu Midekisa (4474249)

September 2018

Classifications were made using the country specific cutoffs at which CV-classification accuracy was...

Performance of the Super Learner and the top 5 individual models in predicting the quantitative log IC50 outcome.

Craig A. Magaret (111759)
David C. Benkeser (6533012)
Brian D. Williamson (6533015)
Bhavesh R. Borate (6533018)
Lindsay N. Carpp (298877)
Ivelin S. Georgiev (688784)
Ian Setliff (6533021)
Adam S. Dingens (3294717)
Noah Simon (685914)
Marco Carone (251182)
Christopher Simpkins (6533024)
David Montefiori (161606)
Galit Alter (91383)
Wen-Han Yu (234340)
Michal Juraska (688780)
Paul T. Edlefsen (650757)
Shelly Karuna (3094875)
Nyaradzo M. Mgodi (3331383)
Srilatha Edugupanti (6533027)
Peter B. Gilbert (30581)

April 2019

Cross-validated R2 point estimates and 95% confidence intervals are shown for A) models trained on d...

Classification performance of the three observed approaches.

Gregor Stiglic (327658)
Petra Povalej Brzan (834962)
Nino Fijacko (834963)
Fei Wang (19534)
Boris Delibasic (834964)
Alexandros Kalousis (834965)
Zoran Obradovic (834966)

December 2015

Four sets of boxplots represent predictive performance measured in Area under the ROC curve (AUC)...

Box plots of features selected in 1000 (100%) training folds by the best performing classification model.

Mattias Nilsson Benfatto (3235731)
Gustaf Öqvist Seimyr (3235725)
Jan Ygge (3235728)
Tony Pansell (3235734)
Agneta Rydberg (3235722)
Christer Jacobson (3235737)

December 2016

The box plots show the distribution of values, normalized to range between 0 and 1, by feature an...

Boxplot showing the predictive performance measures for prediction models.

Marcio Luis Acencio (380824)
Luiz Augusto Bovolenta (477002)
Esther Camilo (477003)
Ney Lemke (92374)

October 2013

Boxplot showing the distribution of recall, precision and AUC values for 1000 prediction models g...

Summary of the classification results in cross-validation on the learning sets and on the independent test sets using the “All binary” or “Two-tier” method.

Nathalie Jeanray (682816)
Raphaël Marée (32623)
Benoist Pruvot (682817)
Olivier Stern (682818)
Pierre Geurts (29807)
Louis Wehenkel (32624)
Marc Muller (116959)

January 2015

For each phenotype (column 1), the optimal mode of classification (“BAGS” or “C”) (second column)...

Prediction accuracy of the supervised learning algorithms for the datasets of different sampling resolutions.

Jinxiang Xi (518144)
Weizhong Zhao (801902)

January 2019

(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...

Super Learner

Polley, Eric

January 2010

The super learner is a general loss-based learning method designed to find the optimal combination o...

Overall schema of datasets (boxes) and processes (arrows) that led to the analyses (red).

Naihui Zhou (5570783)
Zachary D. Siegel (5570786)
Scott Zarecor (5570789)
Nigel Lee (633607)
Darwin A. Campbell (74865)
Carson M. Andorf (335871)
Dan Nettleton (6085)
Carolyn J. Lawrence-Dill (4241194)
Baskar Ganapathysubramanian (633609)
Jonathan W. Kelly (5570792)
Iddo Friedberg (9867)

July 2018

Top row: The Expert Labeled dataset was used a gold standard to analyze how well the different ex...

Mean accuracy (MA) boxplots for the different co-training methods.

Weifeng Liu (635311)
Yang Li (7082)
Xu Lin (36043)
Dacheng Tao (331902)
Yanjiang Wang (635312)

September 2014

Each subfigure corresponds to one case of labeled examples. (A) MA obtained using 10% labeled exa...

Performance of NOP models trained using SyNet compared to a shuffled version of this network (i.e. the same genes are present but randomly connected while keeping their degree intact).

Amin Allahyar (2620357)
Joske Ubels (6307664)
Jeroen de Ridder (66819)

February 2019

a. Bars indicate average performance of models across repeats and error bars denote the correspondin...

The effect of Combat harmonization in the external validation dataset on model performance per machine learning classifier for lymph node involvement (LNI), extracapsular extension (ECE), and Gleason score (GS) prediction.

Wietske I. Luining (17364125)
Daniela E. Oprea-Lager (9465294)
André N. Vis (9465291)
Reindert J. A. van Moorselaar (17364128)
Remco J. J. Knol (17364131)
Maurits Wondergem (9465276)
Ronald Boellaard (300971)
Matthijs C. F. Cysouw (17364134)

November 2023

The effect of Combat harmonization in the external validation dataset on model performance per machi...

Comparing predictive performance of statistical learning models on Medical data

Biney, Francis

January 2020

This work investigates the predictive performance of 10 Machine learning models on three medical dat...

Classification boxplots for the best-performing models and the Super Learner in classifying the IC<sub>50</sub> censored outcome.

Abstract

Extracted data

Classification boxplots for the best-performing models and the Super Learner in classifying the IC<sub>50</sub> censored outcome.

Abstract

Extracted data

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