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.

Variational Inference (VI) is widely used in data representation (Kingma and Welling, 2013; Zhang et al., 2018), graphical models (Wainwright et al., 2008), among others. VI approximates intractable distributions by minimizing the divergence between the true posterior and a chosen distribution family, aiming to identify an optimal distribution within this family. Unlike methods like Markov chain Monte Carlo (MCMC) sampling, VI is recognized for its computational efficiency and explicit distribution form (Blei et al., 2017). Contemporary VI-based methods such as variational autoencoder (VAE) (Kingma and Welling, 2013) have garnered interest for learning representations of complex, high-dimensional data across fields like bioinformatics (Kopf et al., 2021), geoscience (Chen et al., 2022), and finance (Bergeron et al., 2022). Automatic Differentiation Variational Inference (ADVI) (Kucukelbir et al., 2017) is a popular approach to derive variational inference algorithms for complex probabilistic models.