BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

191 related articles for article (PubMed ID: 32044914)

  • 1. DeepKinZero: zero-shot learning for predicting kinase-phosphosite associations involving understudied kinases.
    Deznabi I; Arabaci B; Koyutürk M; Tastan O
    Bioinformatics; 2020 Jun; 36(12):3652-3661. PubMed ID: 32044914
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data.
    Yang P; Humphrey SJ; James DE; Yang YH; Jothi R
    Bioinformatics; 2016 Jan; 32(2):252-9. PubMed ID: 26395771
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A global phosphosite-correlated network map of Thousand And One Kinase 1 (TAOK1).
    Priyanka P; Gopalakrishnan AP; Nisar M; Shivamurthy PB; George M; John L; Sanjeev D; Yandigeri T; Thomas SD; Rafi A; Dagamajalu S; Velikkakath AKG; Abhinand CS; Kanekar S; Prasad TSK; Balaya RDA; Raju R
    Int J Biochem Cell Biol; 2024 May; 170():106558. PubMed ID: 38479581
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Pf-Phospho: a machine learning-based phosphorylation sites prediction tool for Plasmodium proteins.
    Gupta P; Venkadesan S; Mohanty D
    Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35753700
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions.
    Zhou Z; Yeung W; Gravel N; Salcedo M; Soleymani S; Li S; Kannan N
    Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36692152
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Tyr352 as a Predominant Phosphosite in the Understudied Kinase and Molecular Target, HIPK1: Implications for Cancer Therapy.
    Sanjeev D; George M; John L; Gopalakrishnan AP; Priyanka P; Mendon S; Yandigeri T; Nisar M; Nisar M; Kanekar S; Balaya RDA; Raju R
    OMICS; 2024 Mar; 28(3):111-124. PubMed ID: 38498023
    [TBL] [Abstract][Full Text] [Related]  

  • 7. IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data.
    Mischnik M; Sacco F; Cox J; Schneider HC; Schäfer M; Hendlich M; Crowther D; Mann M; Klabunde T
    Bioinformatics; 2016 Feb; 32(3):424-31. PubMed ID: 26628587
    [TBL] [Abstract][Full Text] [Related]  

  • 8. In silico analysis of phosphoproteome data suggests a rich-get-richer process of phosphosite accumulation over evolution.
    Yachie N; Saito R; Sugahara J; Tomita M; Ishihama Y
    Mol Cell Proteomics; 2009 May; 8(5):1061-71. PubMed ID: 19136663
    [TBL] [Abstract][Full Text] [Related]  

  • 9. An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation.
    Arshad OA; Danna V; Petyuk VA; Piehowski PD; Liu T; Rodland KD; McDermott JE
    Mol Cell Proteomics; 2019 Aug; 18(8 suppl 1):S26-S36. PubMed ID: 31227600
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites.
    Ayati M; Yilmaz S; Blasco Tavares Pereira Lopes F; Chance M; Koyuturk M
    Pac Symp Biocomput; 2023; 28():73-84. PubMed ID: 36540966
    [TBL] [Abstract][Full Text] [Related]  

  • 11. In Vitro Kinase-to-Phosphosite Database (iKiP-DB) Predicts Kinase Activity in Phosphoproteomic Datasets.
    Mari T; Mösbauer K; Wyler E; Landthaler M; Drosten C; Selbach M
    J Proteome Res; 2022 Jun; 21(6):1575-1587. PubMed ID: 35608653
    [TBL] [Abstract][Full Text] [Related]  

  • 12. MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.
    Wang D; Zeng S; Xu C; Qiu W; Liang Y; Joshi T; Xu D
    Bioinformatics; 2017 Dec; 33(24):3909-3916. PubMed ID: 29036382
    [TBL] [Abstract][Full Text] [Related]  

  • 13. From Phosphosites to Kinases.
    Munk S; Refsgaard JC; Olsen JV; Jensen LJ
    Methods Mol Biol; 2016; 1355():307-21. PubMed ID: 26584935
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Illuminating the Dark Cancer Phosphoproteome Through a Machine-Learned Co-Regulation Map of 26,280 Phosphosites.
    Jiang W; Jaehnig EJ; Liao Y; Yaron-Barir TM; Johnson JL; Cantley LC; Zhang B
    bioRxiv; 2024 Mar; ():. PubMed ID: 38562798
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Quantitative phosphoproteomics-based molecular network description for high-resolution kinase-substrate interactome analysis.
    Narushima Y; Kozuka-Hata H; Tsumoto K; Inoue J; Oyama M
    Bioinformatics; 2016 Jul; 32(14):2083-8. PubMed ID: 27153602
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Kinome-wide identification of phosphorylation networks in eukaryotic proteomes.
    Parca L; Ariano B; Cabibbo A; Paoletti M; Tamburrini A; Palmeri A; Ausiello G; Helmer-Citterich M
    Bioinformatics; 2019 Feb; 35(3):372-379. PubMed ID: 30016513
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The regulatory landscape of the yeast phosphoproteome.
    Leutert M; Barente AS; Fukuda NK; Rodriguez-Mias RA; Villén J
    Nat Struct Mol Biol; 2023 Nov; 30(11):1761-1773. PubMed ID: 37845410
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Scop3P: A Comprehensive Resource of Human Phosphosites within Their Full Context.
    Ramasamy P; Turan D; Tichshenko N; Hulstaert N; Vandermarliere E; Vranken W; Martens L
    J Proteome Res; 2020 Aug; 19(8):3478-3486. PubMed ID: 32508104
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Illuminating the dark phosphoproteome.
    Needham EJ; Parker BL; Burykin T; James DE; Humphrey SJ
    Sci Signal; 2019 Jan; 12(565):. PubMed ID: 30670635
    [TBL] [Abstract][Full Text] [Related]  

  • 20. KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.
    Anandakrishnan M; Ross KE; Chen C; Shanker V; Cowart J; Wu CH
    PeerJ; 2023; 11():e16164. PubMed ID: 37818330
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.