  Human cancers present a significant public health challenge and require the
discovery of novel drugs through translational research. Transcriptomics
profiling data that describes molecular activities in tumors and cancer cell
lines are widely utilized for predicting anti-cancer drug responses. However,
existing AI models face challenges due to noise in transcriptomics data and
lack of biological interpretability. To overcome these limitations, we
introduce VETE (Variational and Explanatory Transcriptomics Encoder), a novel
neural network framework that incorporates a variational component to mitigate
noise effects and integrates traceable gene ontology into the neural network
architecture for encoding cancer transcriptomics data. Key innovations include
a local interpretability-guided method for identifying ontology paths, a
visualization tool to elucidate biological mechanisms of drug responses, and
the application of centralized large scale hyperparameter optimization. VETE
demonstrated robust accuracy in cancer cell line classification and drug
response prediction. Additionally, it provided traceable biological
explanations for both tasks and offers insights into the mechanisms underlying
its predictions. VETE bridges the gap between AI-driven predictions and
biologically meaningful insights in cancer research, which represents a
promising advancement in the field.
