# Representation Transfer for Differentially Private Drug Sensitivity Prediction

## TL;DR

This paper explores using public datasets to learn data representations that improve the accuracy of differentially private drug sensitivity prediction from gene expression data, addressing high-dimensional privacy challenges.

## Contribution

It introduces a method leveraging public data for representation learning to enhance differentially private genomic predictions, comparing VAE, PCA, and random projection.

## Key findings

- Variational autoencoders outperform other methods in prediction accuracy.
- All representation methods significantly improve privacy-preserving drug sensitivity prediction.
- Results surpass previous state-of-the-art accuracy in the field.

## Abstract

Motivation: Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymisation strategies fail to provide sufficient level of protection for genomic data, because the data are inherently identifiable. Differentially private machine learning can help by guaranteeing that the published results do not leak too much information about any individual data point. Recent research has reached promising results on differentially private drug sensitivity prediction using gene expression data. Differentially private learning with genomic data is challenging because it is more difficult to guarantee the privacy in high dimensions. Dimensionality reduction can help, but if the dimension reduction mapping is learned from the data, then it needs to be differentially private too, which can carry a significant privacy cost. Furthermore, the selection of any hyperparameters (such as the target dimensionality) needs to also avoid leaking private information. Results: We study an approach that uses a large public dataset of similar type to learn a compact representation for differentially private learning. We compare three representation learning methods: variational autoencoders, PCA and random projection. We solve two machine learning tasks on gene expression of cancer cell lines: cancer type classification, and drug sensitivity prediction. The experiments demonstrate significant benefit from all representation learning methods with variational autoencoders providing the most accurate predictions most often. Our results significantly improve over previous state-of-the-art in accuracy of differentially private drug sensitivity prediction.