Performance of ML models trained separately on each individual MD run and tested on the other MD runs.

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</p

Correlations between model predictions and validation data, Model R, Model RMSE on Training Models.

David G. Serfass (826285)
Ryne A. Sherman (826286)

November 2015

Note. r = correlations between scores predicted by the model and score for that tweet from...

Mean Squared Error Averaged Across Subjects.

Ben Walker (484674)
Konrad Kording (141310)

November 2013

The results of four separate models run against all experiments in DREAM. The Mean Squared Error ...

Details of 100 idealized simulated datasets.

Susana Barbosa (340521)
William C. Black IV (262504)
Ian Hastings (340524)

February 2013

The simulated datasets were used to check the precision and accuracy of the ML procedure.*...

Forecasting performance comparison by RMSE, MAE and MAPE.

Yan-Ling Zheng (701006)
Li-Ping Zhang (1489732)
Xue-Liang Zhang (701007)
Kai Wang (21246)
Yu-Jian Zheng (701008)

March 2015

RMSE = root mean square error, MAE = mean absolute error and MAPE = mean absolute percentage erro...

Comparison of prediction performance of several models with BSLMM for three traits in the heterogenous stock mouse dataset.

Xiang Zhou (164107)
Peter Carbonetto (280292)
Matthew Stephens (3815)

February 2013

Performance is measured by RMSE difference with respect to BSLMM, where a positive value indicate...

Performance of ML models trained on randomized subsets of increasing size of the complete MD data.

Matthias Rupp (185094)
Matthias R. Bauer (509957)
Rainer Wilcken (509958)
Andreas Lange (509959)
Michael Reutlinger (509960)
Frank M. Boeckler (509961)
Gisbert Schneider (17096)

January 2014

See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003400#pcbi-10034...

Comparison of existing dTm prediction methods dataset statistics Q (accuracy), MAE (mean absolute error), and r2 (pearson correlation).

Kenneth N. McGuinness (5729180)
Weilan Pan (2695894)
Robert P. Sheridan (1271010)
Grant Murphy (5729183)
Alejandro Crespo (1445995)

September 2018

Comparison of existing dTm prediction methods dataset statistics Q (accuracy), MAE (mean absolute er...

Comparison of RMSE for the prediction model on the target NM_002559 with HEGS-based features and features without transfer, under different training data percentages.

Qi Liu (33068)
Han Zhou (109068)
Kui Zhang (46471)
Xiaoxiao Shi (109071)
Wei Fan (109074)
Ruixin Zhu (107945)
Philip S. Yu (109078)
Zhiwei Cao (109083)

December 2012

Comparison of RMSE for the prediction model on the target NM_002559 with HEGS-based features and ...

Statistical parameters of the ten GFA-MLR models.

Wensheng Wu (703888)
Canyang Zhang (703889)
Wenjing Lin (703890)
Quan Chen (163802)
Xindong Guo (703891)
Yu Qian (153240)
Lijuan Zhang (36878)

March 2015

RMSE (a): root mean square error of the training set; RMSE (b): root mean square er...

Root-mean-squared error (RMSE) comparison among different models on the connectivity map gene expression data.

Guibo Ye (498403)
Mengfan Tang (498404)
Jian-Feng Cai (498405)
Qing Nie (23186)
Xiaohui Xie (24409)

December 2013

“Lasso” represents model (2), “Linear low-rank” represents model (3), and “Nonlinear low-rank” re...

Correlations between baseline rMSSD (ms) values and cognitive performance outcomes.

Alison Beaumont (299162)
Alexander R. Burton (299163)
Jim Lemon (299164)
Barbara K. Bennett (299165)
Andrew Lloyd (299166)
Uté Vollmer-Conna (299167)

February 2013

The correlations coefficients (r) are from Pearson correlations; rMSSD = root mean squar...

Performance (mean ± standard deviation) comparison among all competing methods in both experiments.

Liye Wang (714777)
Chong-Yaw Wee (320982)
Heung-Il Suk (523359)
Xiaoying Tang (422959)
Dinggang Shen (204274)

March 2015

The prefix ’A’ denotes the use of age as a kernel matrix. (CC: Correlation Coefficient; RMSE: Roo...

Performance of machine learning models on test set using the original imbalanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the original imbalanced training set.</p

RMS error (in dB) and squared Pearson’s correlation (r2) between data and simulations presented in Fig 5.

Thibaud Necciari (3358679)
Bernhard Laback (3358676)
Sophie Savel (3358685)
Sølvi Ystad (755943)
Peter Balazs (3358688)
Sabine Meunier (3358682)
Richard Kronland-Martinet (755942)

November 2016

RMS error (in dB) and squared Pearson’s correlation (r2) between data and simul...

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</p

Correlations between model predictions and validation data, Model R, Model RMSE on Training Models.

David G. Serfass (826285)
Ryne A. Sherman (826286)

November 2015

Note. r = correlations between scores predicted by the model and score for that tweet from...

Mean Squared Error Averaged Across Subjects.

Ben Walker (484674)
Konrad Kording (141310)

November 2013

The results of four separate models run against all experiments in DREAM. The Mean Squared Error ...

Details of 100 idealized simulated datasets.

Susana Barbosa (340521)
William C. Black IV (262504)
Ian Hastings (340524)

February 2013

The simulated datasets were used to check the precision and accuracy of the ML procedure.*...

Forecasting performance comparison by RMSE, MAE and MAPE.

Yan-Ling Zheng (701006)
Li-Ping Zhang (1489732)
Xue-Liang Zhang (701007)
Kai Wang (21246)
Yu-Jian Zheng (701008)

March 2015

RMSE = root mean square error, MAE = mean absolute error and MAPE = mean absolute percentage erro...

Comparison of prediction performance of several models with BSLMM for three traits in the heterogenous stock mouse dataset.

Xiang Zhou (164107)
Peter Carbonetto (280292)
Matthew Stephens (3815)

February 2013

Performance is measured by RMSE difference with respect to BSLMM, where a positive value indicate...

Performance of ML models trained on randomized subsets of increasing size of the complete MD data.

Matthias Rupp (185094)
Matthias R. Bauer (509957)
Rainer Wilcken (509958)
Andreas Lange (509959)
Michael Reutlinger (509960)
Frank M. Boeckler (509961)
Gisbert Schneider (17096)

January 2014

See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003400#pcbi-10034...

Comparison of existing dTm prediction methods dataset statistics Q (accuracy), MAE (mean absolute error), and r2 (pearson correlation).

Kenneth N. McGuinness (5729180)
Weilan Pan (2695894)
Robert P. Sheridan (1271010)
Grant Murphy (5729183)
Alejandro Crespo (1445995)

September 2018

Comparison of existing dTm prediction methods dataset statistics Q (accuracy), MAE (mean absolute er...

Comparison of RMSE for the prediction model on the target NM_002559 with HEGS-based features and features without transfer, under different training data percentages.

Qi Liu (33068)
Han Zhou (109068)
Kui Zhang (46471)
Xiaoxiao Shi (109071)
Wei Fan (109074)
Ruixin Zhu (107945)
Philip S. Yu (109078)
Zhiwei Cao (109083)

December 2012

Comparison of RMSE for the prediction model on the target NM_002559 with HEGS-based features and ...

Statistical parameters of the ten GFA-MLR models.

Wensheng Wu (703888)
Canyang Zhang (703889)
Wenjing Lin (703890)
Quan Chen (163802)
Xindong Guo (703891)
Yu Qian (153240)
Lijuan Zhang (36878)

March 2015

RMSE (a): root mean square error of the training set; RMSE (b): root mean square er...

Root-mean-squared error (RMSE) comparison among different models on the connectivity map gene expression data.

Guibo Ye (498403)
Mengfan Tang (498404)
Jian-Feng Cai (498405)
Qing Nie (23186)
Xiaohui Xie (24409)

December 2013

“Lasso” represents model (2), “Linear low-rank” represents model (3), and “Nonlinear low-rank” re...

Correlations between baseline rMSSD (ms) values and cognitive performance outcomes.

Alison Beaumont (299162)
Alexander R. Burton (299163)
Jim Lemon (299164)
Barbara K. Bennett (299165)
Andrew Lloyd (299166)
Uté Vollmer-Conna (299167)

February 2013

The correlations coefficients (r) are from Pearson correlations; rMSSD = root mean squar...

Performance (mean ± standard deviation) comparison among all competing methods in both experiments.

Liye Wang (714777)
Chong-Yaw Wee (320982)
Heung-Il Suk (523359)
Xiaoying Tang (422959)
Dinggang Shen (204274)

March 2015

The prefix ’A’ denotes the use of age as a kernel matrix. (CC: Correlation Coefficient; RMSE: Roo...

Performance of machine learning models on test set using the original imbalanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the original imbalanced training set.</p

RMS error (in dB) and squared Pearson’s correlation (r2) between data and simulations presented in Fig 5.

Thibaud Necciari (3358679)
Bernhard Laback (3358676)
Sophie Savel (3358685)
Sølvi Ystad (755943)
Peter Balazs (3358688)
Sabine Meunier (3358682)
Richard Kronland-Martinet (755942)

November 2016

RMS error (in dB) and squared Pearson’s correlation (r2) between data and simul...

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.

Nirajan Budhathoki (17599014)
Ramesh Bhandari (11080476)
Suraj Bashyal (17599017)
Carl Lee (17599020)

December 2023

Performance of machine learning models on test set using the ROSE-adjusted balanced training set.</p

Correlations between model predictions and validation data, Model R, Model RMSE on Training Models.

David G. Serfass (826285)
Ryne A. Sherman (826286)

November 2015

Note. r = correlations between scores predicted by the model and score for that tweet from...

Mean Squared Error Averaged Across Subjects.

Ben Walker (484674)
Konrad Kording (141310)

November 2013

The results of four separate models run against all experiments in DREAM. The Mean Squared Error ...

Performance of ML models trained separately on each individual MD run and tested on the other MD runs.

Abstract

Extracted data

Performance of ML models trained separately on each individual MD run and tested on the other MD runs.

Abstract

Extracted data

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