About: Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction
Creator	Wang, Yuyang Magar, Rishikesh Yadav, Prakarsh Farimani, Amir
source	BioRxiv
abstract	Viral pandemics are emerging as a serious global threat to public health, like the recent outbreak of COVID-19. Viruses, especially those belonging to a large family of +ssRNA viruses, have a high possibility of mutating by inserting, deleting, or substituting one or multiple genome segments. It is of great importance for human health worldwide to predict the possible virus mutations, which can effectively avoid the potential second outbreak. In this work, we develop a GAN-based multi-class protein sequence generative model, named ProteinSeqGAN. Given the viral species, the generator is modeled on RNNs to predict the corresponding antigen epitope sequences synthesized by viral genomes. Additionally, a Graphical Protein Autoencoder (GProAE) built upon VAE is proposed to featurize proteins bioinformatically. GProAE, as a multi-class discriminator, also learns to evaluate the goodness of protein sequences and predict the corresponding viral species. Further experiments show that our ProteinSeqGAN model can generate valid antigen protein sequences from both bioinformatics and statistics perspectives, which can be promising predictions of virus mutations.
has issue date	2020-06-12(xsd:dateTime)
bibo:doi	10.1101/2020.06.11.146167
has license	biorxiv
sha1sum (hex)	889fab29fe2eee93228db1ae56698edfa5310a42
schema:url	https://doi.org/10.1101/2020.06.11.146167
resource representing a document's title	Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction
schema:publication	bioRxiv
resource representing a document's body	covid:889fab29fe2eee93228db1ae56698edfa5310a42#body_text
is schema:about of	named entity 'substituting' named entity 'perspectives' named entity 'COVID-19' named entity 'GLOBAL' named entity 'OUR' »more»

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