# Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses

arXiv: https://arxiv.org/abs/2407.04486 · HTML: https://mlsanity.com/paper/2407.04486/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 83 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [How Does Batch Normalization Help Optimization?](https://mlsanity.com/paper/1805.11604/) (2018)
- [True to the Model or True to the Data?](https://mlsanity.com/paper/2006.16234/) (2020)
- [General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models](https://mlsanity.com/paper/2007.04131/) (2020)
- [Towards a Theoretical Understanding of the Robustness of Variational Autoencoders](https://mlsanity.com/paper/2007.07365/) (2020)
- [Deep learning methods for drug response prediction in cancer: predominant and emerging trends](https://mlsanity.com/paper/2211.10442/) (2022)
- [A Closer Look at the Adversarial Robustness of Information Bottleneck Models](https://mlsanity.com/paper/2107.05712/) (2021)
- [Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition](https://mlsanity.com/paper/2204.12365/) (2022)
- [Nonparametric Automatic Differentiation Variational Inference with Spline Approximation](https://mlsanity.com/paper/2403.06302/) (2024)

## Abstract (arXiv)

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.