ROC curve analysis between BD and UD at the cutoff of 7 with three parts of the MDQ scored.

ROC curve analysis of each baseline parameter and prediction of hyperbilirubinemia.

Hironao Okubo (5154833)
Hitoshi Ando (307066)
Yushi Sorin (5154836)
Eisuke Nakadera (5154830)
Hiroo Fukada (5154824)
Junichi Morishige (5154827)
Akihisa Miyazaki (5154821)
Kenichi Ikejima (92830)

April 2018

ROC curve analysis of each baseline parameter and prediction of hyperbilirubinemia.</p

ROC curve showing optimal cut-off value of 3 mm (sensitivity, 0.795; specificity, 0.61).

Hajime Ushigome (6321017)
Yosuke Fukunaga (4056700)
Toshiya Nagasaki (3438119)
Takashi Akiyoshi (4056697)
Tsuyoshi Konishi (4056691)
Yoshiya Fujimoto (4056703)
Satoshi Nagayama (105355)
Masashi Ueno (4056694)

February 2019

ROC curve showing optimal cut-off value of 3 mm (sensitivity, 0.795; specificity, 0.61).</p

ROC curve illustrating the area under the curve for disorder strength and cancer prediction.

Richard Kalman (5233475)
Andrew Stawarz (5233484)
David Nunes (3716305)
Di Zhang (197959)
Mart A. Dela Cruz (5233481)
Arpan Mohanty (5233478)
Hariharan Subramanian (292812)
Vadim Backman (305612)
Hemant K. Roy (305615)

May 2018

ROC curve illustrating the area under the curve for disorder strength and cancer prediction.</p

ROC curve analysis between BD-II and UD with only part one of the MDQ scored.

Hai-Chen Yang (317383)
Tie-Bang Liu (521250)
Han Rong (547396)
Jian-Qiang Bi (547397)
Er-Ni Ji (547398)
Hong-Jun Peng (547399)
Xiao-Ping Wang (317385)
Yi-Ru Fang (317382)
Cheng-Mei Yuan (547400)
Tian-Mei Si (317379)
Zheng Lu (123354)
Jian Hu (31535)
Zhi-Yu Chen (317384)
Yi Huang (112882)
Jing Sun (141206)
Hui-Chun Li (317386)
Chen Hu (317374)
Jin-Bei Zhang (317387)
Ling-Jiang Li (547401)

April 2014

ROC curve analysis between BD-II and UD with only part one of the MDQ scored.</p

ROC curve analysis between BD-I and BD-II with only part one of the MDQ scored.

Hai-Chen Yang (317383)
Tie-Bang Liu (521250)
Han Rong (547396)
Jian-Qiang Bi (547397)
Er-Ni Ji (547398)
Hong-Jun Peng (547399)
Xiao-Ping Wang (317385)
Yi-Ru Fang (317382)
Cheng-Mei Yuan (547400)
Tian-Mei Si (317379)
Zheng Lu (123354)
Jian Hu (31535)
Zhi-Yu Chen (317384)
Yi Huang (112882)
Jing Sun (141206)
Hui-Chun Li (317386)
Chen Hu (317374)
Jin-Bei Zhang (317387)
Ling-Jiang Li (547401)

April 2014

ROC curve analysis between BD-I and BD-II with only part one of the MDQ scored.</p

ROC curve analysis for the association of the modified DIC score with the development of DIC.

Offer Erez (212386)
Lena Novack (550638)
Ruthy Beer-Weisel (550639)
Doron Dukler (550640)
Fernanda Press (550641)
Alexander Zlotnik (550642)
Nandor Gabor Than (212381)
Aaron Tomer (550643)
Moshe Mazor (550644)

April 2014

ROC curve analysis for the association of the modified DIC score with the development of DIC.</p

ROC curve analysis for BMI in men. Figure 3b. ROC curve analysis for BMI in women.

Angel A. López (327097)
Mey L. Cespedes (327098)
Teofila Vicente (327099)
Matias Tomas (327100)
Miguel Bennasar-Veny (327101)
Pedro Tauler (327102)
Antoni Aguilo (327104)

February 2013

ROC curve analysis for BMI in men. Figure 3b. ROC curve analysis for BMI in women.</p

ROC curve analysis.

Bernhard Hametner (819993)
Stephanie Parragh (819994)
Thomas Weber (819995)
Siegfried Wassertheurer (819998)

June 2017

Left: Comparison of the ROC curves obtained with central pulse pressure (PP), ejection duration (...

ROC curve for discrimination between a) T2DM-NP and control, b) T2DM and control and c) T2DM and T2DM-NP.

Nariman Moradi (3391952)
Reza Fadaei (3391958)
Mohammad Ebrahim Khamseh (6619679)
Ali Nobakht (6619682)
Mohammad Jafar Rezaei (6619685)
Fereshteh Aliakbary (6619688)
Akram Vatannejad (6078212)
Jalil Hosseini (6619691)

April 2019

ROC curve for discrimination between a) T2DM-NP and control, b) T2DM and control and c) T2DM and T2D...

ROC curve comparison diagram.

Zhining Zhang (10583870)
Liang Wan (119472)
Kun Chu (11727827)
Shusheng Li (503955)
Haodong Wei (14259630)
Lu Tang (655389)

December 2022

(a) The result of the first fold PR curve. (b) The result of the second fold PR curve. (c) The resul...

ROC curve using the reference of 5.56% as cut-off point in defining diagnostic performance.

Bien Van Tran (11269128)
Kouichi Ujita (11269131)
Ayako Taketomi-Takahashi (11269134)
Hiromi Hirasawa (11269137)
Takayuki Suto (10135730)
Yoshito Tsushima (531550)

August 2021

ROC curve using the reference of 5.56% as cut-off point in defining diagnostic performance.</p

ROC curve analysis: Plot of sensitivity versus 1-specificity (PD-Q scale).

Amani Abu-Shaheen (4966288)
Shehu Yousef (4966282)
Muhammad Riaz (104236)
Abdullah Nofal (4966291)
Isamme AlFayyad (5130824)
Sarfaraz Khan (4966285)
Humariya Heena (4966294)

April 2018

ROC curve analysis: Plot of sensitivity versus 1-specificity (PD-Q scale).</p

ROC curve and the area under the curve (AUC) under different distributional assumptions for the BRCA data.

Mojtaba Ganjali (550736)
Taban Baghfalaki (667319)
Damon Berridge (729703)

April 2015

ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...

ROC curve analysis for the diagnostic abilities of base SBF values in differentiating between healthy volunteers and SS patients.

Yukiko N. Kami (2558107)
Misa Sumi (662395)
Yukinori Takagi (2558104)
Miho Sasaki (72561)
Masataka Uetani (2558110)
Takashi Nakamura (8496)

March 2016

ROC curve analysis for the diagnostic abilities of base SBF values in differentiating between hea...

ROC curve analysis to determine cutoff score for 14-3-3σ and EZH2 expression.

Yi Zhang (9093)
Yang Li (7082)
Changwei Lin (630203)
Jie Ding (73711)
Guoqing Liao (630204)
Bo Tang (23472)

September 2014

(A, C) EZH2 cutoff point of OS and DFS, the EZH2 cutoff score for OS and DFS was 4 (p = 0....

ROC curve analysis of each baseline parameter and prediction of hyperbilirubinemia.

Hironao Okubo (5154833)
Hitoshi Ando (307066)
Yushi Sorin (5154836)
Eisuke Nakadera (5154830)
Hiroo Fukada (5154824)
Junichi Morishige (5154827)
Akihisa Miyazaki (5154821)
Kenichi Ikejima (92830)

April 2018

ROC curve analysis of each baseline parameter and prediction of hyperbilirubinemia.</p

ROC curve showing optimal cut-off value of 3 mm (sensitivity, 0.795; specificity, 0.61).

Hajime Ushigome (6321017)
Yosuke Fukunaga (4056700)
Toshiya Nagasaki (3438119)
Takashi Akiyoshi (4056697)
Tsuyoshi Konishi (4056691)
Yoshiya Fujimoto (4056703)
Satoshi Nagayama (105355)
Masashi Ueno (4056694)

February 2019

ROC curve showing optimal cut-off value of 3 mm (sensitivity, 0.795; specificity, 0.61).</p

ROC curve illustrating the area under the curve for disorder strength and cancer prediction.

Richard Kalman (5233475)
Andrew Stawarz (5233484)
David Nunes (3716305)
Di Zhang (197959)
Mart A. Dela Cruz (5233481)
Arpan Mohanty (5233478)
Hariharan Subramanian (292812)
Vadim Backman (305612)
Hemant K. Roy (305615)

May 2018

ROC curve illustrating the area under the curve for disorder strength and cancer prediction.</p

ROC curve analysis between BD-II and UD with only part one of the MDQ scored.

Hai-Chen Yang (317383)
Tie-Bang Liu (521250)
Han Rong (547396)
Jian-Qiang Bi (547397)
Er-Ni Ji (547398)
Hong-Jun Peng (547399)
Xiao-Ping Wang (317385)
Yi-Ru Fang (317382)
Cheng-Mei Yuan (547400)
Tian-Mei Si (317379)
Zheng Lu (123354)
Jian Hu (31535)
Zhi-Yu Chen (317384)
Yi Huang (112882)
Jing Sun (141206)
Hui-Chun Li (317386)
Chen Hu (317374)
Jin-Bei Zhang (317387)
Ling-Jiang Li (547401)

April 2014

ROC curve analysis between BD-II and UD with only part one of the MDQ scored.</p

ROC curve analysis between BD-I and BD-II with only part one of the MDQ scored.

Hai-Chen Yang (317383)
Tie-Bang Liu (521250)
Han Rong (547396)
Jian-Qiang Bi (547397)
Er-Ni Ji (547398)
Hong-Jun Peng (547399)
Xiao-Ping Wang (317385)
Yi-Ru Fang (317382)
Cheng-Mei Yuan (547400)
Tian-Mei Si (317379)
Zheng Lu (123354)
Jian Hu (31535)
Zhi-Yu Chen (317384)
Yi Huang (112882)
Jing Sun (141206)
Hui-Chun Li (317386)
Chen Hu (317374)
Jin-Bei Zhang (317387)
Ling-Jiang Li (547401)

April 2014

ROC curve analysis between BD-I and BD-II with only part one of the MDQ scored.</p

ROC curve analysis for the association of the modified DIC score with the development of DIC.

Offer Erez (212386)
Lena Novack (550638)
Ruthy Beer-Weisel (550639)
Doron Dukler (550640)
Fernanda Press (550641)
Alexander Zlotnik (550642)
Nandor Gabor Than (212381)
Aaron Tomer (550643)
Moshe Mazor (550644)

April 2014

ROC curve analysis for the association of the modified DIC score with the development of DIC.</p

ROC curve analysis for BMI in men. Figure 3b. ROC curve analysis for BMI in women.

Angel A. López (327097)
Mey L. Cespedes (327098)
Teofila Vicente (327099)
Matias Tomas (327100)
Miguel Bennasar-Veny (327101)
Pedro Tauler (327102)
Antoni Aguilo (327104)

February 2013

ROC curve analysis for BMI in men. Figure 3b. ROC curve analysis for BMI in women.</p

ROC curve analysis.

Bernhard Hametner (819993)
Stephanie Parragh (819994)
Thomas Weber (819995)
Siegfried Wassertheurer (819998)

June 2017

Left: Comparison of the ROC curves obtained with central pulse pressure (PP), ejection duration (...

ROC curve for discrimination between a) T2DM-NP and control, b) T2DM and control and c) T2DM and T2DM-NP.

Nariman Moradi (3391952)
Reza Fadaei (3391958)
Mohammad Ebrahim Khamseh (6619679)
Ali Nobakht (6619682)
Mohammad Jafar Rezaei (6619685)
Fereshteh Aliakbary (6619688)
Akram Vatannejad (6078212)
Jalil Hosseini (6619691)

April 2019

ROC curve for discrimination between a) T2DM-NP and control, b) T2DM and control and c) T2DM and T2D...

ROC curve comparison diagram.

Zhining Zhang (10583870)
Liang Wan (119472)
Kun Chu (11727827)
Shusheng Li (503955)
Haodong Wei (14259630)
Lu Tang (655389)

December 2022

(a) The result of the first fold PR curve. (b) The result of the second fold PR curve. (c) The resul...

ROC curve using the reference of 5.56% as cut-off point in defining diagnostic performance.

Bien Van Tran (11269128)
Kouichi Ujita (11269131)
Ayako Taketomi-Takahashi (11269134)
Hiromi Hirasawa (11269137)
Takayuki Suto (10135730)
Yoshito Tsushima (531550)

August 2021

ROC curve using the reference of 5.56% as cut-off point in defining diagnostic performance.</p

ROC curve analysis: Plot of sensitivity versus 1-specificity (PD-Q scale).

Amani Abu-Shaheen (4966288)
Shehu Yousef (4966282)
Muhammad Riaz (104236)
Abdullah Nofal (4966291)
Isamme AlFayyad (5130824)
Sarfaraz Khan (4966285)
Humariya Heena (4966294)

April 2018

ROC curve analysis: Plot of sensitivity versus 1-specificity (PD-Q scale).</p

ROC curve and the area under the curve (AUC) under different distributional assumptions for the BRCA data.

Mojtaba Ganjali (550736)
Taban Baghfalaki (667319)
Damon Berridge (729703)

April 2015

ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...

ROC curve analysis for the diagnostic abilities of base SBF values in differentiating between healthy volunteers and SS patients.

Yukiko N. Kami (2558107)
Misa Sumi (662395)
Yukinori Takagi (2558104)
Miho Sasaki (72561)
Masataka Uetani (2558110)
Takashi Nakamura (8496)

March 2016

ROC curve analysis for the diagnostic abilities of base SBF values in differentiating between hea...

ROC curve analysis to determine cutoff score for 14-3-3σ and EZH2 expression.

Yi Zhang (9093)
Yang Li (7082)
Changwei Lin (630203)
Jie Ding (73711)
Guoqing Liao (630204)
Bo Tang (23472)

September 2014

(A, C) EZH2 cutoff point of OS and DFS, the EZH2 cutoff score for OS and DFS was 4 (p = 0....

ROC curve analysis of each baseline parameter and prediction of hyperbilirubinemia.

Hironao Okubo (5154833)
Hitoshi Ando (307066)
Yushi Sorin (5154836)
Eisuke Nakadera (5154830)
Hiroo Fukada (5154824)
Junichi Morishige (5154827)
Akihisa Miyazaki (5154821)
Kenichi Ikejima (92830)

April 2018

ROC curve analysis of each baseline parameter and prediction of hyperbilirubinemia.</p

ROC curve showing optimal cut-off value of 3 mm (sensitivity, 0.795; specificity, 0.61).

Hajime Ushigome (6321017)
Yosuke Fukunaga (4056700)
Toshiya Nagasaki (3438119)
Takashi Akiyoshi (4056697)
Tsuyoshi Konishi (4056691)
Yoshiya Fujimoto (4056703)
Satoshi Nagayama (105355)
Masashi Ueno (4056694)

February 2019

ROC curve showing optimal cut-off value of 3 mm (sensitivity, 0.795; specificity, 0.61).</p

ROC curve illustrating the area under the curve for disorder strength and cancer prediction.

Richard Kalman (5233475)
Andrew Stawarz (5233484)
David Nunes (3716305)
Di Zhang (197959)
Mart A. Dela Cruz (5233481)
Arpan Mohanty (5233478)
Hariharan Subramanian (292812)
Vadim Backman (305612)
Hemant K. Roy (305615)

May 2018

ROC curve illustrating the area under the curve for disorder strength and cancer prediction.</p

ROC curve analysis between BD and UD at the cutoff of 7 with three parts of the MDQ scored.

Abstract

Extracted data

ROC curve analysis between BD and UD at the cutoff of 7 with three parts of the MDQ scored.

Abstract

Extracted data

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