DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach.
Brief Bioinform
; 22(6)2021 11 05.
Article
in English
| MEDLINE | ID: covidwho-1286553
ABSTRACT
The rapid spread of SARS-CoV-2 infection around the globe has caused a massive health and socioeconomic crisis. Identification of phosphorylation sites is an important step for understanding the molecular mechanisms of SARS-CoV-2 infection and the changes within the host cells pathways. In this study, we present DeepIPs, a first specific deep-learning architecture to identify phosphorylation sites in host cells infected with SARS-CoV-2. DeepIPs consists of the most popular word embedding method and convolutional neural network-long short-term memory network architecture to make the final prediction. The independent test demonstrates that DeepIPs improves the prediction performance compared with other existing tools for general phosphorylation sites prediction. Based on the proposed model, a web-server called DeepIPs was established and is freely accessible at http//lin-group.cn/server/DeepIPs. The source code of DeepIPs is freely available at the repository https//github.com/linDing-group/DeepIPs.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Phosphorylation
/
Software
/
SARS-CoV-2
/
COVID-19 Drug Treatment
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Bib
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