Figure 5 (IMAGE)
Caption
Data analysis pipeline outlining key data processing, machine learning analysis, and validation steps used in the study. Exclusion criteria are described further in the Methods. Patients were grouped according to cancer type and were split into training, tuning and validation cohorts using a stratified semi-random approach. The P142 panel was then applied to patients in each cohort separately and the TSM results (the collective output of the panel) were collated. For each cancer type, the XGBoost algorithm was used to train and tune models using the training and tuning cohorts, then the final model was evaluated using the validation cohort. This process was repeated (20 rounds) using a different patient split in each case. The barplot illustrates predictions for validation patients (prediction values between 0 and 1) with patients predicted to be "Low PFS" below the red line and patients predicted as "High PFS" above the red line. Bars are colored according to their actual PFS status: red = "Low PFS" and green = "High PFS".
Credit
Correspondence to - Jared Mamrot - jared.mamrot@gmdxgen.com
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Copyright: © 2021 Mamrot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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