Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A multitask transfer learning framework for the prediction of
  virus-human protein-protein interactions   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.

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The categories of this paper are: stat
32
The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: quant-ph