Additional file 5: of Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA
Nathan Wan
David Weinberg
Tzu-Yu Liu
Katherine Niehaus
Eric Ariazi
Daniel Delubac
Ajay Kannan
Brandon White
Mitch Bailey
Marvin Bertin
Nathan Boley
Derek Bowen
James Cregg
Adam Drake
Riley Ennis
Signe Fransen
Erik Gafni
Loren Hansen
Yaping Liu
Gabriel Otte
Jennifer Pecson
Brandon Rice
Gabriel Sanderson
Aarushi Sharma
John St. John
Catherina Tang
Abraham Tzou
Leilani Young
Girish Putcha
Imran Haque
10.6084/m9.figshare.9728531.v1
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)
2019-08-24 03:52:50
Cell-free DNA
Colorectal cancer
Screening
Whole-genome sequencing
Early-stage cancer