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. 2022 Jun 1;12:883437. doi: 10.3389/fonc.2022.883437

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Copyright © 2022 Geevimaan, Guo, Shen, Jiang, Fann, Hwang, Shui, Lin, Wang, Shih, Li, Chang, Yang and Chen

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Oxaliplatin response predictor genes identified by Leave-One-Out Cross Validation (LOOCV) algorithm. (A) Workflow of identification of oxaliplatin response signatures using LOOCV algorithm to compare genes and transcripts differentially expressed between OR and OS PDOs. (B) Machine learning algorithms to evaluate the accuracy of predictor response genes and transcripts with their scores. (C) Oxaliplatin response predictor genes identified by LOOCV algorithm, along with their mean expression values in the OR and OS PDOs and log₂[fold change] with adjusted P < 0.005. (D) Heat map of oxaliplatin response predictor genes identified by LOOCV algorithm.