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.
%0 Journal Article
%1 dong21
%A Dong, Ngan Thi
%A Brogden, Graham
%A Gerold, Gisa
%A Khosla, Megha
%D 2021
%J BMC Bioinformatics
%K l3s leibnizailab myown
%N 1
%P 572
%R 10.1186/s12859-021-04484-y
%T A multitask transfer learning framework for the prediction of virus-human protein--protein interactions
%U https://doi.org/10.1186/s12859-021-04484-y
%V 22
%X 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.
@article{dong21,
abstract = {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.},
added-at = {2021-11-27T14:35:20.000+0100},
author = {Dong, Ngan Thi and Brogden, Graham and Gerold, Gisa and Khosla, Megha},
bdsk-url-1 = {https://doi.org/10.1186/s12859-021-04484-y},
bdsk-url-2 = {http://dx.doi.org/10.1186/s12859-021-04484-y},
biburl = {https://www.bibsonomy.org/bibtex/2d4f161f60ff96155393c227a8b40e2cf/khosla},
da = {2021/11/27},
date-added = {2021-11-27 13:34:46 +0000},
date-modified = {2021-11-27 13:34:46 +0000},
doi = {10.1186/s12859-021-04484-y},
id = {Dong2021},
interhash = {54a1acff21ab3f0afc3b305d9ba60610},
intrahash = {d4f161f60ff96155393c227a8b40e2cf},
journal = {BMC Bioinformatics},
keywords = {l3s leibnizailab myown},
number = 1,
pages = 572,
timestamp = {2021-11-27T14:36:19.000+0100},
title = {A multitask transfer learning framework for the prediction of virus-human protein--protein interactions},
ty = {JOUR},
url = {https://doi.org/10.1186/s12859-021-04484-y},
volume = 22,
year = 2021
}