Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network-Reference-Cited by-同舟云学术

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network

Published:2024-03-08 Issue: Volume: Page:
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Deng Qian,Zhang Jing,Liu Jie,Liu Yuqi,Dai Zong,Zou Xiaoyong,Li Zhanchao^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12539-024-00615-0.pdf

Reference62 articles.

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2. Guo X, He H, Yu J et al (2022) PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis. Brief Bioinform 23(1):bbab436. https://doi.org/10.1093/bib/bbab436

3. Audagnotto M, Dal Peraro M (2017) Protein post-translational modifications: in silico prediction tools and molecular modeling. Comput Struct Biotechnol J 15:307–319. https://doi.org/10.1016/j.csbj.2017.03.004

4. Beck-Sickinger AG, Mörl K (2006) Posttranslational modification of proteins. Expanding nature’s inventory. Angew Chem Int Ed 45(7):1020–1020. https://doi.org/10.1002/anie.200585363

5. Pearlman SM, Serber Z, Ferrell JE Jr (2011) A mechanism for the evolution of phosphorylation sites. Cell 147(4):934–946. https://doi.org/10.1016/j.cell.2011.08.052

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