Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer

TL;DR

This paper introduces a scalable, permutation-invariant multi-output Gaussian Process model with a deep generative component for predicting drug combination responses in cancer, capable of handling missing data and new drugs.

Contribution

It develops a variational approximation for multi-output Gaussian Processes, enabling scalable, uncertainty-aware dose-response predictions and generalization to unseen drugs and combinations.

Findings

Model efficiently borrows information across outputs.

Handles missing data naturally.

Demonstrates good performance on high-throughput dataset.

Abstract

Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of \textit{in-vitro} drug sensitivity screens, the goal is to develop accurate predictive models that can be used to guide experimental design or inform treatment decisions. Building on previous work that makes use of permutation invariant multi-output Gaussian Processes in the context of dose-response prediction for drug combinations, we develop a variational approximation to these models. The variational approximation enables a more scalable model that provides uncertainty quantification and naturally handles missing data. Furthermore, we propose using a deep generative model to encode the chemical space in a continuous manner, enabling prediction for new drugs and new combinations. We demonstrate the performance of our model in a simple setting using a high-throughput dataset and show that the model is able to efficiently borrow information across outputs.