Fig. 4: Using top functional groups to yield a simple and interpretable linear model. | Nature Communications

Fig. 4: Using top functional groups to yield a simple and interpretable linear model.

From: Pretrained transformers applied to clinical studies improve predictions of treatment efficacy and associated biomarkers

Fig. 4

a CoxPH model trained on top 10 functional groups derived from Clinical Transformer trained on Samstein et al.33 discovery dataset. Bar plot (mean) depicts distribution of each CoxPH coefficient across only the train splits where the coefficient p-value < 0.05 (Wald test); black dots: individual data points; KM curves of patients from test splits of the 10 models for Samstein et al.33 discovery dataset, stratified by median training set risk score (partial hazard) from the CoxPH model. b KM curves of independent pan-cancer validation datasets, stratified by the CoxPH (trained on Samstein et al. top 10 functional groups) median training set risk score or by TMB in Miao et al.73. IO-treated or TCGA treatment-naïve patients. c, KM curves of independent, melanoma-only validation datasets, stratified by CoxPH (trained on Samstein et al. top 10 functional groups) median training set risk score or by TMB in IO-treated (Miao et al., Van Allen et al.), or treatment-naive (TCGA) patients. Shaded region or error bars in (a): 95% confidence interval. HR p-values in (b, c) from Wald statistical test. TMB cutoff in (b, c) = 10 mut/mb. Source data are provided in the SourceData file.

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