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DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach.
Lv, Hao; Dao, Fu-Ying; Zulfiqar, Hasan; Lin, Hao.
  • Lv H; Center for Informational Biology at the University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Dao FY; Center for Informational Biology at the University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Zulfiqar H; Center for Informational Biology at the University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Lin H; Center for Informational Biology at the University of Electronic Science and Technology of China, Chengdu 610054, China.
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.
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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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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