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 *

491 related articles for article (PubMed ID: 17360525)

  • 1. Predicting protein-protein interactions based only on sequences information.
    Shen J; Zhang J; Luo X; Zhu W; Yu K; Chen K; Li Y; Jiang H
    Proc Natl Acad Sci U S A; 2007 Mar; 104(11):4337-41. PubMed ID: 17360525
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of Protein-Protein Interaction via co-occurring Aligned Pattern Clusters.
    Sze-To A; Fung S; Lee EA; Wong AKC
    Methods; 2016 Nov; 110():26-34. PubMed ID: 27476008
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences.
    Guo Y; Yu L; Wen Z; Li M
    Nucleic Acids Res; 2008 May; 36(9):3025-30. PubMed ID: 18390576
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.
    Li ZW; You ZH; Chen X; Gui J; Nie R
    Int J Mol Sci; 2016 Aug; 17(9):. PubMed ID: 27571061
    [TBL] [Abstract][Full Text] [Related]  

  • 5. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences.
    An JY; You ZH; Meng FR; Xu SJ; Wang Y
    Int J Mol Sci; 2016 May; 17(5):. PubMed ID: 27213337
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.
    Li ZW; You ZH; Chen X; Li LP; Huang DS; Yan GY; Nie R; Huang YA
    Oncotarget; 2017 Apr; 8(14):23638-23649. PubMed ID: 28423569
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.
    Zhang M; Su Q; Lu Y; Zhao M; Niu B
    Med Chem; 2017; 13(6):506-514. PubMed ID: 28530547
    [TBL] [Abstract][Full Text] [Related]  

  • 8. SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.
    Li X; Han P; Wang G; Chen W; Wang S; Song T
    BMC Genomics; 2022 Jun; 23(1):474. PubMed ID: 35761175
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest.
    You ZH; Chan KC; Hu P
    PLoS One; 2015; 10(5):e0125811. PubMed ID: 25946106
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation.
    Li Y; Wang Z; You ZH; Li LP; Hu X
    Comput Math Methods Med; 2022; 2022():7191684. PubMed ID: 35242211
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.
    Wang J; Zhang L; Jia L; Ren Y; Yu G
    Int J Mol Sci; 2017 Nov; 18(11):. PubMed ID: 29117139
    [TBL] [Abstract][Full Text] [Related]  

  • 12. LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.
    Tsukiyama S; Hasan MM; Fujii S; Kurata H
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34160596
    [TBL] [Abstract][Full Text] [Related]  

  • 13. PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.
    Wang Y; You Z; Li X; Chen X; Jiang T; Zhang J
    Int J Mol Sci; 2017 May; 18(5):. PubMed ID: 28492483
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information.
    An JY; You ZH; Chen X; Huang DS; Yan G; Wang DF
    Mol Biosyst; 2016 Nov; 12(12):3702-3710. PubMed ID: 27759121
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition.
    Huang YA; You ZH; Chen X; Yan GY
    BMC Syst Biol; 2016 Dec; 10(Suppl 4):120. PubMed ID: 28155718
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Highly Efficient Framework for Predicting Interactions Between Proteins.
    Zhu-Hong You ; MengChu Zhou ; Xin Luo ; Shuai Li
    IEEE Trans Cybern; 2017 Mar; 47(3):731-743. PubMed ID: 28113829
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting protein-protein interactions between human and hepatitis C virus via an ensemble learning method.
    Emamjomeh A; Goliaei B; Zahiri J; Ebrahimpour R
    Mol Biosyst; 2014 Dec; 10(12):3147-54. PubMed ID: 25230581
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting protein-protein interactions using high-quality non-interacting pairs.
    Zhang L; Yu G; Guo M; Wang J
    BMC Bioinformatics; 2018 Dec; 19(Suppl 19):525. PubMed ID: 30598096
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Amalgamation of 3D structure and sequence information for protein-protein interaction prediction.
    Jha K; Saha S
    Sci Rep; 2020 Nov; 10(1):19171. PubMed ID: 33154416
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A correlated motif approach for finding short linear motifs from protein interaction networks.
    Tan SH; Hugo W; Sung WK; Ng SK
    BMC Bioinformatics; 2006 Nov; 7():502. PubMed ID: 17107624
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 25.