10.6084/m9.figshare.9728531.v1
Nathan Wan
Nathan
Wan
David Weinberg
David
Weinberg
Tzu-Yu Liu
Tzu-Yu
Liu
Katherine Niehaus
Katherine
Niehaus
Eric Ariazi
Eric
Ariazi
Daniel Delubac
Daniel
Delubac
Ajay Kannan
Ajay
Kannan
Brandon White
Brandon
White
Mitch Bailey
Mitch
Bailey
Marvin Bertin
Marvin
Bertin
Nathan Boley
Nathan
Boley
Derek Bowen
Derek
Bowen
James Cregg
James
Cregg
Adam Drake
Adam
Drake
Riley Ennis
Riley
Ennis
Signe Fransen
Signe
Fransen
Erik Gafni
Erik
Gafni
Loren Hansen
Loren
Hansen
Yaping Liu
Yaping
Liu
Gabriel Otte
Gabriel
Otte
Jennifer Pecson
Jennifer
Pecson
Brandon Rice
Brandon
Rice
Gabriel Sanderson
Gabriel
Sanderson
Aarushi Sharma
Aarushi
Sharma
John St. John
John
St. John
Catherina Tang
Catherina
Tang
Abraham Tzou
Abraham
Tzou
Leilani Young
Leilani
Young
Girish Putcha
Girish
Putcha
Imran Haque
Imran
Haque
Additional file 5: of Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA
Springer Nature
2019
Cell-free DNA
Colorectal cancer
Screening
Whole-genome sequencing
Early-stage cancer
2019-08-24 03:52:50
Journal contribution
https://springernature.figshare.com/articles/journal_contribution/Additional_file_5_of_Machine_learning_enables_detection_of_early-stage_colorectal_cancer_by_whole-genome_sequencing_of_plasma_cell-free_DNA/9728531
Figure S4. Non-linear relationship between the total number of samples used for training and sensitivity at 85% specificity for colorectal cancer detection. The method was trained again with k-fold, except the number of training samples per fold was downsampled. The lower numbers are comparable to those available for balanced k-batch and were used to investigate decreased classifier performance due to smaller sample sizes in training. (DOCX 60 kb)