Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development

TL;DR

This paper proposes using human cell line transcriptome data combined with Bayesian Gaussian process models to predict animal toxicity, aiming to improve trustworthiness and reduce animal testing in drug development.

Contribution

It introduces a novel approach integrating transcriptomic features with Bayesian models for toxicity prediction, enhancing model transparency and reliability.

Findings

Gaussian processes effectively predict animal toxicity from human cell line data.

Feature set selection significantly impacts model accuracy.

The approach can potentially reduce animal testing in drug development.

Abstract

Biomedical data, particularly in the field of genomics, has characteristics which make it challenging for machine learning applications - it can be sparse, high dimensional and noisy. Biomedical applications also present challenges to model selection - whilst powerful, accurate predictions are necessary, they alone are not sufficient for a model to be deemed useful. Due to the nature of the predictions, a model must also be trustworthy and transparent, empowering a practitioner with confidence that its use is appropriate and reliable. In this paper, we propose that this can be achieved through the use of judiciously built feature sets coupled with Bayesian models, specifically Gaussian processes. We apply Gaussian processes to drug discovery, using inexpensive transcriptomic profiles from human cell lines to predict animal kidney and liver toxicity after treatment with specific chemical compounds. This approach has the potential to reduce invasive and expensive animal testing during clinical trials if in vitro human cell line analysis can accurately predict model animal phenotypes. We compare results across a range of feature sets and models, to highlight model importance for medical applications.