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

@article{Dong2021AMT,
  title={A multitask transfer learning framework for novel virus-human protein interactions},
  author={Ngan Thi Dong and Megha Khosla},
  journal={bioRxiv},
  year={2021}
}
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.

Sharing to learn and learning to share - Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning : A meta review

The global generic learning network – an amalgamation of meta learning, transfer learning, and multi-task learning – is introduced here, along with some open research questions and future research directions in the multi- task setting.

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.

References

SHOWING 1-10 OF 85 REFERENCES

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

Based on the amino acid sequence of proteins and the currently known virus-human PPI network, Seq-BEL calculates various features and similarities of human proteins and viral proteins, and then combines these similarities and features to score the potential of virus- human PPIs.

Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences

The proposed DeepVHPPI, a novel deep learning framework combining a self-attention-based transformer architecture and a transfer learning training strategy to predict interactions between human proteins and virus proteins that have novel sequence patterns, outperforms the state-of-the-art methods significantly in predicting Virus–Human protein interactions.

DeNovo: virus-host sequence-based protein-protein interaction prediction

DeNovo is a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins, and achieves near optimal accuracy when tested on bacteria-human interactions.

Predict the Protein-protein Interaction between Virus and Host through Hybrid Deep Neural Network

A hybrid deep learning framework which combines convolutional neural network together with a long short term memory network to Extract more hidden high-level features was designed to extract more latent features and is superior to the current advanced framework in both benchmark data and independent testing.

Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data.

This study formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data and provides more than 10 percent improvements for accuracy and AUC results in comparison with state-of-the-art methods.

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

A probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model, which is more robust against data unavailability with less demanding data constraint.

Prediction of Interactions between Viral and Host Proteins Using Supervised Machine Learning Methods

The proposed SVM-based method can predict large scale interspecies viral-human PPIs and the nature and function of unknown viral proteins, interacting partners of host protein were identified using optimised SVM model.

Machine-learning techniques for the prediction of protein–protein interactions

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