Work

Supplementary Figure S1, Tables S3, S5 and S6 for "Machine Learning–Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity"

Year: 2025

Type: supplementary-materials

Abstract: Overview - Table_S3_List_of_cell_lines_v1.0_2025-08-21.xlsx, List of cell lines included in the analyses. - Table_S5_Results_of_DEG_analysis_between_cluster_2_(Resistant)_and_cluster_4_(Sensitive)_using_pyDESeq2_v1.0_2025-08-21.xlsx, Differential expression analysis results between cluster 2 (resistant) and cluster 4 (sensitive) using pyDESeq2. - Table_S6_List_of_SHAP_values_for_each_gene_based_on_machine_learning_model_v1.0_2025-08-21.xlsx, SHAP values per gene derived from the machine learning model. Keywords cisplatin, machine learning, omics, RNAseq, SHAP, LightGBM, TCGA

Source: Cancer Medicine

DOI: 10.5281/zenodo.16786026

Authors: Ujiie, Haruki, Nishiya, Naoyuki

Institution: Iwate Medical University

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,700