Attentive Variational Information Bottleneck for TCR-peptide interaction prediction.

ABSTRACT:

Motivation

We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides.

Results

Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amin

SUBMITTER: Grazioli F 

PROVIDER: S-EPMC9825246 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature