Seq-BEL: Sequence-Based Ensemble Learning for Predicting Virus-Human Protein-Protein Interaction

Weighted hypergraph learning and adaptive inductive matrix completion for SARS-CoV-2 drug repositioning

MOTIVATION

The outbreak of the human coronavirus (SARS-CoV-2) has placed a huge burden on public health and the world economy. Compared with de novo drug discovery, drug repurposing is a promising therapeutic strategy that facilitates rapid clinical treatment decisions, shortens the development process, and reduces costs.

RESULTS

In this study, we propose a weighted hypergraph learning and adaptive inductive matrix completion method, WHAIMC, for predicting potential virus-drug associations. Firstly, we integrate multi-source data to describe viruses and drugs from multiple perspectives, including drug chemical structures, drug targets, virus complete genome sequences, and virus-drug associations. Then, WHAIMC establishes an adaptive inductive matrix completion model to improve performance through adaptive learning of similarity relations. Finally, WHAIMC introduces weighted hypergraph learning into adaptive inductive matrix completion to capture higher-order relationships of viruses (or drugs). The results showed that WHAIMC had a strong predictive performance for new virus-drug associations, new viruses, and new drugs. The case study further demonstrates that WHAIMC is highly effective for repositioning antiviral drugs against SARS-CoV-2 and provides a new perspective for virus-drug association prediction. The code and data in this study is freely available at https://github.com/Mayingjun20179/WHAIMC.

A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction

Protein-protein interaction is one of the ways viruses interact with their hosts. Therefore, identifying protein interactions between viruses and hosts helps explain how virus proteins work, how they replicate, and how they cause disease. SARS-CoV-2 is a new type of virus that emerged from the coronavirus family in 2019 and caused a worldwide pandemic. Detection of human proteins interacting with this novel virus strain plays an important role in monitoring the cellular process of virus-associated infection. Within the scope of the study, a natural language processing-based collective learning method is proposed for the prediction of potential SARS-CoV-2-human PPIs. Protein language models were obtained with the prediction-based word2Vec and doc2Vec embedding methods and the frequency-based tf-idf method. Known interactions were represented by proposed language models and traditional feature extraction methods (conjoint triad and repeat pattern), and their performances were compared. The interaction data were trained with support vector machine, artificial neural network (ANN), k-nearest neighbor (KNN), naive Bayes (NB), decision tree (DT), and ensemble algorithms. Experimental results show that protein language models are a promising protein representation method for protein-protein interaction prediction. The term frequency-inverse document frequency-based language model performed the SARS-CoV-2 protein-protein interaction estimation with an error of 1.4%. Additionally, the decisions of high-performing learning models for different feature extraction methods were combined with a collective voting approach to make new interaction predictions. For 10,000 human proteins, 285 new potential interactions were predicted, with models combining decisions.

DeepMNE: Deep Multi-network Embedding for lncRNA-Disease Association prediction

Long non-coding RNA (lncRNA) participates in various biological processes, hence its mutations and disorders play an important role in the pathogenesis of multiple human diseases. Identifying disease-related lncRNAs is crucial for the diagnosis, prevention, and treatment of diseases. Although a large number of computational approaches have been developed, effectively integrating multi-omics data and accurately predicting potential lncRNA-disease associations remains a challenge, especially regarding new lncRNAs and new diseases. In this work, we propose a new method with deep multi-network embedding, called DeepMNE, to discover potential lncRNA disease associations, especially for novel diseases and lncRNAs. DeepMNE extracts multi-omics data to describe diseases and lncRNAs, and proposes a network fusion method based on deep learning to integrate multi-source information. Moreover, DeepMNE complements the sparse association network and uses kernel neighborhood similarity to construct disease similarity and lncRNA similarity networks. Furthermore, A graph embedding method is adopted to predict potential associations. Experimental results demonstrate that compared to other state-of-the-art methods, DeepMNE has a higher predictive performance on new associations, new lncRNAs and new diseases. Besides, DeepMNE also elicits a considerable predictive performance on perturbed datasets. Additionally, the results of two different types of case studies indicate that DeepMNE can be used as an effective tool for disease-related lncRNA prediction. The code of DeepMNE is freely available at https://github.com/Mayingjun20179/ DeepMNE.

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

BACKGROUND

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 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.

RESULTS

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.

CONCLUSIONS

Our approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-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 .