Fig. 1

A machine learning (ML) approach for predicting aneuploidy in cancer. A Schematic view of the ML model construction. Labels represent aneuploidy status of each chromosome arm in 24 cancer types (abbreviation of cancer types detailed in Additional file 2: Table S1), classified as gained (red, n = 199), lost (blue, n = 307), or neutral (white, n = 430). Features consist of 20 types of features pertaining to chromosome-arms, normal tissues and cancer tissues (see B). Two separate ML models were constructed to predict gained and lost chromosome-arms (gain model and loss model). Each model was analyzed to estimate the contribution of the features to the predicted outcome. B The features analyzed by the ML model. The inner layer shows feature categories: chromosome arms (purple), cancer tissues (primary tumors and CCLs, blue), and normal tissues (green). The middle layer shows the sub-categories of the features. Chromosome-arm features include essentiality and driver genes features. Cancer-tissue features include transcriptomics and essentiality features. Normal-tissue features include protein–protein interactions (PPIs), transcriptomics, paralogs, eQTL, tissue-specific (TS) genes, development, and GO processes features. The outer layer represents all 20 feature types that were analyzed by the model. Numbers in parentheses indicate the number of tissues, organs, or cell lines from which cancer and normal tissue features were derived, or the number of chromosome-arms from which chromosome-arm features were derived. C The performance of the ML models as evaluated by the area under the receiver-operating characteristic curve (auROC, left) and the precision recall curve (auPRC, right) using tenfold cross-validation. Gain model (gradient boosting): auROC = 74% and auPRC = 63% (expected 32%). Loss model (XGBoost): auROC = 70% and auPRC = 63% (expected 42%)