Extended Data Fig. 1: Features and performance of Translatomer model.

a, Sketch plot showing the architecture of the transformer layer used in this study. The full Translatomer model is shown in Fig. 1a. b, Model evaluation based on Spearman correlation (left) and MSE loss (right), between Translatomer and other cutting-edge ribosome profiling prediction models, including iXnos, RiboMIMO and Riboformer, using an 11-fold cross-validation strategy in K562, epithelial cells, and brain datasets. The bars represent the mean values, and the error bars represent the standard errors. Each bar contains 11 replicates derived from the 11-fold cross-validation. c, Comparison of the key features between Translatomer and other ribosome profiling prediction models. d, Pearson correlation coefficient (PCC) increases and converges as the number of training epoch increases. Multi-input and single-input models are in blue and red, respectively. Training accuracy is represented by a dotted line and validation accuracy is represented by a solid line. The accuracy difference between multi-input and single-input models is calculated based on the validation accuracy. e, Mean-squared error loss decreases and converges upon the increase of training epochs. f, Table showing the performance of different hyper-parameters tested during model construction using the datasets from 33 tissues and cell lines.