Additional file 4 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Additional file 6: Table S2. of A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

Gregory Way (2880575)
Robert Allaway (3721711)
Stephanie Bouley (3721714)
Camilo Fadul (3721708)
Yolanda Sanchez (213896)
Casey Greene (2880572)

February 2017

Ranked genes contributing greater than two standard deviations to ensemble classifier. (XLSX 27Â kb

Additional file 3: of MRCNN: a deep learning model for regression of genome-wide DNA methylation

Qi Tian (123361)
Jianxiao Zou (6574103)
Jianxiong Tang (1465777)
Yuan Fang (132924)
Zhongli Yu (6574106)
Shicai Fan (81690)

April 2019

Additional figures. Comparison of the classification performances in three cancerous tissues. (PDF 5...

Additional file 5 of Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers

Hossein Bonakdari (13773708)
Jean-Pierre Pelletier (918)
Francisco J. Blanco (22726)
Ignacio Rego-Pérez (140742)
Alejandro Durán-Sotuela (13773711)
Dawn Aitken (3448724)
Graeme Jones (15001)
Flavia Cicuttini (3448730)
Afshin Jamshidi (8940161)
François Abram (84277)
Johanne Martel-Pelletier (12614157)

September 2022

Additional file 5. Methods of the implementation of the machine learning classification methodologie...

Additional file 3 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 3. PC-3 initial dataset with the 123 RDKit descriptors calculate

Additional file 2 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 2. Table with DU-145 ChEMBL assays used to retrieve compounds activitie

Additional file 6 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 6. Python script to generate the initial PC-3, DU-145 and PC-3/DU-145 dataset

Additional file 1 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 1. Table with PC-3 ChEMBL assays used to retrieve compounds activitie

Additional file 8 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 8. Python script to perform training and testing phase of the combined PC-3/DU-145 m...

Additional file 10 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 10. Python script to setup algorithms input parameters for the hyperparameter tuning...

Machine learning models for risk stratification of prostate cancers

Muthamilselvan S
Palaniappan A

April 2023

RDS objects (i.e, R binary files) of better-performing models for prostate cancer detection and aggr...

Additional file 1: of Using machine learning algorithms to identify genes essential for cell survival

Santosh Philips (4489522)
Heng-Yi Wu (737267)
Lang Li (91927)

October 2017

The file contains three sheets labelled Table S1 - S3. The tables list the genes that are currently ...

Additional file 1 of Clinical application of machine learning models in patients with prostate cancer before prostatectomy

Adalgisa Guerra (17938361)
Matthew R. Orton (8814047)
Helen Wang (240809)
Marianna Konidari (17938364)
Kris Maes (17938367)
Nickolas K. Papanikolaou (17938370)
Dow Mu Koh (5832332)

February 2024

Supplementary Material 1: Table S1. Standardised institutional MR image sequence parameters for Pros...

Additional file 21: of Exploring targets of TET2-mediated methylation reprogramming as potential discriminators of prostate cancer progression

Shivani Kamdar (3423416)
Ruth Isserlin (237394)
Theodorus Kwast (3423422)
Alexandre Zlotta (3423419)
Gary Bader (3423413)
Neil Fleshner (361896)
Bharati Bapat (112041)

March 2019

Figure S14. Multidimensional scaling (MDS) plot depicting Euclidean distances (similarities) between...

Additional file 3: of Predict drug sensitivity of cancer cells with pathway activity inference

Xuewei Wang (195027)
Zhifu Sun (60356)
Michael Zimmermann (203279)
Andrej Bugrim (247869)
Jean-Pierre Kocher (179225)

February 2019

Coefficients of pathways in elastic net models (based on DiffRank) for each CCLE drug. Pathways are ...

Additional file 1 of Machine learning in medicine: a practical introduction

Jenni A. M. Sidey-Gibbons (6486689)
Chris J. Sidey-Gibbons (6331097)

March 2019

Breast Cancer Wisconsin Dataset. Anonomised dataset used in this work. (CSV 24.9 kb

Additional file 6: Table S2. of A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

Gregory Way (2880575)
Robert Allaway (3721711)
Stephanie Bouley (3721714)
Camilo Fadul (3721708)
Yolanda Sanchez (213896)
Casey Greene (2880572)

February 2017

Ranked genes contributing greater than two standard deviations to ensemble classifier. (XLSX 27Â kb

Additional file 3: of MRCNN: a deep learning model for regression of genome-wide DNA methylation

Qi Tian (123361)
Jianxiao Zou (6574103)
Jianxiong Tang (1465777)
Yuan Fang (132924)
Zhongli Yu (6574106)
Shicai Fan (81690)

April 2019

Additional figures. Comparison of the classification performances in three cancerous tissues. (PDF 5...

Additional file 5 of Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers

Hossein Bonakdari (13773708)
Jean-Pierre Pelletier (918)
Francisco J. Blanco (22726)
Ignacio Rego-Pérez (140742)
Alejandro Durán-Sotuela (13773711)
Dawn Aitken (3448724)
Graeme Jones (15001)
Flavia Cicuttini (3448730)
Afshin Jamshidi (8940161)
François Abram (84277)
Johanne Martel-Pelletier (12614157)

September 2022

Additional file 5. Methods of the implementation of the machine learning classification methodologie...

Additional file 3 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 3. PC-3 initial dataset with the 123 RDKit descriptors calculate

Additional file 2 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 2. Table with DU-145 ChEMBL assays used to retrieve compounds activitie

Additional file 6 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 6. Python script to generate the initial PC-3, DU-145 and PC-3/DU-145 dataset

Additional file 1 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 1. Table with PC-3 ChEMBL assays used to retrieve compounds activitie

Additional file 8 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 8. Python script to perform training and testing phase of the combined PC-3/DU-145 m...

Additional file 10 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Davide Bonanni (5491928)
Luca Pinzi (6268934)
Giulio Rastelli (1513714)

November 2022

Additional file 10. Python script to setup algorithms input parameters for the hyperparameter tuning...

Machine learning models for risk stratification of prostate cancers

Muthamilselvan S
Palaniappan A

April 2023

RDS objects (i.e, R binary files) of better-performing models for prostate cancer detection and aggr...

Additional file 1: of Using machine learning algorithms to identify genes essential for cell survival

Santosh Philips (4489522)
Heng-Yi Wu (737267)
Lang Li (91927)

October 2017

The file contains three sheets labelled Table S1 - S3. The tables list the genes that are currently ...

Additional file 1 of Clinical application of machine learning models in patients with prostate cancer before prostatectomy

Adalgisa Guerra (17938361)
Matthew R. Orton (8814047)
Helen Wang (240809)
Marianna Konidari (17938364)
Kris Maes (17938367)
Nickolas K. Papanikolaou (17938370)
Dow Mu Koh (5832332)

February 2024

Supplementary Material 1: Table S1. Standardised institutional MR image sequence parameters for Pros...

Additional file 21: of Exploring targets of TET2-mediated methylation reprogramming as potential discriminators of prostate cancer progression

Shivani Kamdar (3423416)
Ruth Isserlin (237394)
Theodorus Kwast (3423422)
Alexandre Zlotta (3423419)
Gary Bader (3423413)
Neil Fleshner (361896)
Bharati Bapat (112041)

March 2019

Figure S14. Multidimensional scaling (MDS) plot depicting Euclidean distances (similarities) between...

Additional file 3: of Predict drug sensitivity of cancer cells with pathway activity inference

Xuewei Wang (195027)
Zhifu Sun (60356)
Michael Zimmermann (203279)
Andrej Bugrim (247869)
Jean-Pierre Kocher (179225)

February 2019

Coefficients of pathways in elastic net models (based on DiffRank) for each CCLE drug. Pathways are ...

Additional file 1 of Machine learning in medicine: a practical introduction

Jenni A. M. Sidey-Gibbons (6486689)
Chris J. Sidey-Gibbons (6331097)

March 2019

Breast Cancer Wisconsin Dataset. Anonomised dataset used in this work. (CSV 24.9 kb

Additional file 6: Table S2. of A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

Gregory Way (2880575)
Robert Allaway (3721711)
Stephanie Bouley (3721714)
Camilo Fadul (3721708)
Yolanda Sanchez (213896)
Casey Greene (2880572)

February 2017

Ranked genes contributing greater than two standard deviations to ensemble classifier. (XLSX 27Â kb

Additional file 3: of MRCNN: a deep learning model for regression of genome-wide DNA methylation

Qi Tian (123361)
Jianxiao Zou (6574103)
Jianxiong Tang (1465777)
Yuan Fang (132924)
Zhongli Yu (6574106)
Shicai Fan (81690)

April 2019

Additional figures. Comparison of the classification performances in three cancerous tissues. (PDF 5...

Additional file 5 of Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers

Hossein Bonakdari (13773708)
Jean-Pierre Pelletier (918)
Francisco J. Blanco (22726)
Ignacio Rego-Pérez (140742)
Alejandro Durán-Sotuela (13773711)
Dawn Aitken (3448724)
Graeme Jones (15001)
Flavia Cicuttini (3448730)
Afshin Jamshidi (8940161)
François Abram (84277)
Johanne Martel-Pelletier (12614157)

September 2022

Additional file 5. Methods of the implementation of the machine learning classification methodologie...

Additional file 4 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Abstract

Extracted data

Additional file 4 of Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Abstract

Extracted data

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