Recent advancements in predicting protein phosphorylation sites using machine learning methods-Reference-Cited by-同舟云学术

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Recent advancements in predicting protein phosphorylation sites using machine learning methods

Published:2021-11-09 Issue: Volume: Page:
ISSN:
Container-title:2021 International Conference on Innovative Computing (ICIC)
language:
Short-container-title:

Author:

Yousaf Adil¹,Rasheed Muhammad Rashid²,Arif Muhammad³,Yousafzai Abdullah⁴,Kabir Muhammad⁴,Ahmed Saeed⁴

Affiliation:

1. Uninversity of Buner,Department of Computer Science,Buner Swari,Pakistan

2. Tiangong Uninversity,School of Computer Science and Technology,Tianjin,P.R.China

3. University of Mangment and Technology,School of System and Technolog,Department of Informatics,Lahore,Pakistan

4. University of Mangment and Technology,School of System and Technolog,Department of Computer Science,Lahore,Pakistan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9692906/9691502/09693016.pdf?arnumber=9693016

Reference61 articles.

1. Characteristics of SARS-CoV-2 and COVID-19;hu;Nat Rev Microbiol,2020

2. DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information

3. DeepCPPred: a deep learning framework for the discrimination of cell-penetrating peptides and their uptake efficiencies

4. Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information

5. RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest

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