A multitask transfer learning framework for novel virus-human protein interactions

  title={A multitask transfer learning framework for novel virus-human protein interactions},
  author={Ngan Thi Dong and Megha Khosla},
Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection. This could further help in developing treatments of viral diseases. The main issues in tackling it as a machine learning problem is the scarcity of training data as well input information of the viral proteins. We overcome these limitations by exploiting powerful statistical protein representations derived from a corpus of around 24… 

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

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 and works effectively for both virus-human and bacteria-human protein–protein interaction prediction tasks.

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Application of Sequence Embedding in Protein Sequence-Based Predictions

Different approaches of protein sequence embeddings and their applications including protein contact prediction, secondary structure, prediction, and function prediction are reviewed.



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Transfer Learning with MotifTransformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans

This work proposes a novel deep learning architecture designed for in silico PPI prediction and a transfer learning approach to predict interactions between novel virus proteins and human proteins and shows that it outperforms the state-of-the-art methods significantly in predicting Virus-Human protein interactions for SARS-CoV-2, H1N1, and Ebola.

DeepViral: prediction of novel virus–host interactions from protein sequences and infectious disease phenotypes

DeepViral is developed, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses that significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction.

Probability Weighted Ensemble Transfer Learning for Predicting Interactions between HIV-1 and Human Proteins

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Machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.