These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

146 related articles for article (PubMed ID: 38148797)

  • 21. DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning.
    Xie Y; Luo X; Li Y; Chen L; Ma W; Huang J; Cui J; Zhao Y; Xue Y; Zuo Z; Ren J
    Genomics Proteomics Bioinformatics; 2018 Aug; 16(4):294-306. PubMed ID: 30268931
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Methodologies for the characterization, identification and quantification of S-nitrosylated proteins.
    Foster MW
    Biochim Biophys Acta; 2012 Jun; 1820(6):675-83. PubMed ID: 21440604
    [TBL] [Abstract][Full Text] [Related]  

  • 23. pSuc-EDBAM: Predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module.
    Jia J; Wu G; Li M; Qiu W
    BMC Bioinformatics; 2022 Oct; 23(1):450. PubMed ID: 36316638
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Predicting S-nitrosylation proteins and sites by fusing multiple features.
    Qiu WR; Wang QK; Guan MY; Jia JH; Xiao X
    Math Biosci Eng; 2021 Oct; 18(6):9132-9147. PubMed ID: 34814339
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Comprehensive identification and modified-site mapping of S-nitrosylated targets in prostate epithelial cells.
    Lam YW; Yuan Y; Isaac J; Babu CV; Meller J; Ho SM
    PLoS One; 2010 Feb; 5(2):e9075. PubMed ID: 20140087
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Mini-review: Recent advances in post-translational modification site prediction based on deep learning.
    Meng L; Chan WS; Huang L; Liu L; Chen X; Zhang W; Wang F; Cheng K; Sun H; Wong KC
    Comput Struct Biotechnol J; 2022; 20():3522-3532. PubMed ID: 35860402
    [TBL] [Abstract][Full Text] [Related]  

  • 27. DGA-5mC: A 5-methylcytosine site prediction model based on an improved DenseNet and bidirectional GRU method.
    Jia J; Qin L; Lei R
    Math Biosci Eng; 2023 Mar; 20(6):9759-9780. PubMed ID: 37322910
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Quantitative site-specific reactivity profiling of S-nitrosylation in mouse skeletal muscle using cysteinyl peptide enrichment coupled with mass spectrometry.
    Su D; Shukla AK; Chen B; Kim JS; Nakayasu E; Qu Y; Aryal U; Weitz K; Clauss TR; Monroe ME; Camp DG; Bigelow DJ; Smith RD; Kulkarni RN; Qian WJ
    Free Radic Biol Med; 2013 Apr; 57():68-78. PubMed ID: 23277143
    [TBL] [Abstract][Full Text] [Related]  

  • 29. The Relationship Between Protein S-Nitrosylation and Human Diseases: A Review.
    Zhang Y; Deng Y; Yang X; Xue H; Lang Y
    Neurochem Res; 2020 Dec; 45(12):2815-2827. PubMed ID: 32984933
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Assessment and application of the biotin switch technique for examining protein S-nitrosylation under conditions of pharmacologically induced oxidative stress.
    Forrester MT; Foster MW; Stamler JS
    J Biol Chem; 2007 May; 282(19):13977-83. PubMed ID: 17376775
    [TBL] [Abstract][Full Text] [Related]  

  • 31. nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.
    Chen YZ; Wang ZZ; Wang Y; Ying G; Chen Z; Song J
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34002774
    [TBL] [Abstract][Full Text] [Related]  

  • 32. An improved sulfur-nitroso-proteome strategy for global profiling of sulfur-nitrosylated proteins and sulfur-nitrosylation sites in mice.
    Yang H; Wang L; Xie Z; Shao S; Wu Y; Xu W; Gu B; Wang B
    J Chromatogr A; 2023 Aug; 1705():464162. PubMed ID: 37336129
    [TBL] [Abstract][Full Text] [Related]  

  • 33. ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning.
    Jia X; Zhao P; Li F; Qin Z; Ren H; Li J; Miao C; Zhao Q; Akutsu T; Dou G; Chen Z; Song J
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36880172
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Identification of S-nitrosylation sites based on multiple features combination.
    Li T; Song R; Yin Q; Gao M; Chen Y
    Sci Rep; 2019 Feb; 9(1):3098. PubMed ID: 30816267
    [TBL] [Abstract][Full Text] [Related]  

  • 35. The SNO/SOH TMT strategy for combinatorial analysis of reversible cysteine oxidations.
    Wojdyla K; Williamson J; Roepstorff P; Rogowska-Wrzesinska A
    J Proteomics; 2015 Jan; 113():415-34. PubMed ID: 25449835
    [TBL] [Abstract][Full Text] [Related]  

  • 36. DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder.
    Khan ZU; Pi D
    Protein Pept Lett; 2021; 28(6):708-721. PubMed ID: 33267753
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Protein S-nitrosylation and denitrosylation in the mouse spinal cord upon injury of the sciatic nerve.
    Scheving R; Wittig I; Heide H; Albuquerque B; Steger M; Brandt U; Tegeder I
    J Proteomics; 2012 Jul; 75(13):3987-4004. PubMed ID: 22588120
    [TBL] [Abstract][Full Text] [Related]  

  • 38. CRMSNet: A deep learning model that uses convolution and residual multi-head self-attention block to predict RBPs for RNA sequence.
    Pan Z; Zhou S; Zou H; Liu C; Zang M; Liu T; Wang Q
    Proteins; 2023 Aug; 91(8):1032-1041. PubMed ID: 36935548
    [TBL] [Abstract][Full Text] [Related]  

  • 39. pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm.
    Jia J; Wu G; Qiu W
    Front Cell Dev Biol; 2022; 10():894874. PubMed ID: 35686053
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Computational prediction of candidate proteins for S-nitrosylation in Arabidopsis thaliana.
    Chaki M; Kovacs I; Spannagl M; Lindermayr C
    PLoS One; 2014; 9(10):e110232. PubMed ID: 25333472
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 8.