Fig. 1: Study classification methodology overview.
a, Combined multi-omic dataset of 6,363 processed features spanning clinical and surgical pathology, SNV, CNV, INDEL, RNA, fusion, tissue proteins, plasma proteins, lipids and computational pathology analytes. b, Construction of all possible analyte combinations (n = 1,024) via a drop-column importance approach to simulate availability of various combinations of analytes. c, For each analyte combination, seven independent ML models were trained for model evaluation, including SVM, principal-component analysis (PCA) + logistic regression, L1-normalized SVM, L1-normalized RF, five-hidden-layer deep neural network, RFE logistic regression and RFE RF. d, Input analyte combinations (n = 1,024) with seven modeling strategies per analyte combination produced 7,168 resulting grid search runs that were subsequently analyzed for predictive power, analyte composition and feature contributions for DS prediction. e, Each unique analyte combination and ML strategy was trained via leave-one-patient-out cross-validation approach. Single-omic and multi-omic models for DS prediction were validated using testing sets from four separate cohorts, TCGA, JHU cohort 1, JHU cohort 2 and the MGH cohort. Clin. & surg. path., clinical and surgical pathology; comp. path., computational pathology; prot., protein.
