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