AutoTransOP: translating omics signatures without orthologue requirements using deep learning

Nikolaos Meimetis, Krista M Pullen, Daniel Zhu, Avlant Nilsson, Trong Nghia Hoang, Sara Magliacane, Douglas Lauffenburger

Keywords: AI for Health, AI for Biology, Systems Biology, Systems Translation, Bioinformatics, Autoencoders, Deep Learning

TL;DR: AutoTransOP is a deep-learning autoencoder that learns a shared latent space to translate omics data between species or cell models, predicting cross-system molecular features without needing orthologue mapping. ​

Abstract:

The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology, as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP (published in npj Systems Biology & Applications: https://doi.org/10.1038/s41540-024-00341-9 ), a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts, most importantly, without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.

Submission Number: 19