Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques
Fig 8
Workflow diagram (A) Data Selection: We acquired diverse datasets for training PASO from multiple databases.
(B) Data Preprocessing: Statistical methods were employed to calculate the differences of various omics data within and outside biological pathways. These pathway-based difference values were utilized as cell line features. Additionally, pytoda was used to process SMILES chemical structure information. (C) Model Architecture: The model is presented in three sections, from top to bottom: The upper section illustrates the SMILES Encoding Network. The middle section depicts the overall model workflow. The lower section details the internal network structure of SMAN. (D) Evaluation of Predictions and Attention Weight Analysis: We evaluated the model using three distinct data partitioning strategies. Subsequently, we conducted drug efficacy analysis and attention weight analysis on the predicted results.