# A multitask transfer learning framework for the prediction of virus-human protein-protein interactions

arXiv: https://arxiv.org/abs/2111.13346 · HTML: https://mlsanity.com/paper/2111.13346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 85 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Training large margin host-pathogen protein-protein interaction predictors](https://mlsanity.com/paper/1711.07886/) (2017)

## Built on by (most cited first, 1 of 1)

- [Sequence-based protein-protein interaction prediction and its applications in drug discovery](https://mlsanity.com/paper/2507.19805/) (2025)

## Abstract (arXiv)

Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in the prevention and treatment of virus-related diseases. However, the task of predicting protein-protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein-protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein-protein interaction prediction model. Our approach achieved competitive results on 13 benchmark datasets and the case study for the SAR-CoV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein-protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer.