BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

249 related articles for article (PubMed ID: 27506469)

  • 1. IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction.
    Pan X; Fan YX; Yan J; Shen HB
    BMC Genomics; 2016 Aug; 17():582. PubMed ID: 27506469
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.
    Yi HC; You ZH; Huang DS; Li X; Jiang TH; Li LP
    Mol Ther Nucleic Acids; 2018 Jun; 11():337-344. PubMed ID: 29858068
    [TBL] [Abstract][Full Text] [Related]  

  • 3. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.
    Pan X; Shen HB
    BMC Bioinformatics; 2017 Feb; 18(1):136. PubMed ID: 28245811
    [TBL] [Abstract][Full Text] [Related]  

  • 4. BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.
    Zhan ZH; Jia LN; Zhou Y; Li LP; Yi HC
    Int J Mol Sci; 2019 Feb; 20(4):. PubMed ID: 30813451
    [TBL] [Abstract][Full Text] [Related]  

  • 5. RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.
    Shen WJ; Cui W; Chen D; Zhang J; Xu J
    Molecules; 2018 Feb; 23(3):. PubMed ID: 29495575
    [TBL] [Abstract][Full Text] [Related]  

  • 6. RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.
    Peng C; Han S; Zhang H; Li Y
    Int J Mol Sci; 2019 Mar; 20(5):. PubMed ID: 30832218
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.
    Asim MN; Ibrahim MA; Imran Malik M; Dengel A; Ahmed S
    Int J Mol Sci; 2021 Aug; 22(16):. PubMed ID: 34445436
    [TBL] [Abstract][Full Text] [Related]  

  • 8. DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy.
    Cheng S; Zhang L; Tan J; Gong W; Li C; Zhang X
    Comput Biol Chem; 2019 Dec; 83():107088. PubMed ID: 31330489
    [TBL] [Abstract][Full Text] [Related]  

  • 9. EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.
    Wang J; Zhao Y; Gong W; Liu Y; Wang M; Huang X; Tan J
    BMC Bioinformatics; 2021 Mar; 22(1):133. PubMed ID: 33740884
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.
    Li Y; Zhang Q; Liu Z; Wang C; Han S; Ma Q; Du W
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33367506
    [TBL] [Abstract][Full Text] [Related]  

  • 11. RNA-binding protein recognition based on multi-view deep feature and multi-label learning.
    Yang H; Deng Z; Pan X; Shen HB; Choi KS; Wang L; Wang S; Wu J
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32808039
    [TBL] [Abstract][Full Text] [Related]  

  • 12. RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction.
    Li X; Qu W; Yan J; Tan J
    J Chem Inf Model; 2024 Apr; 64(7):2221-2235. PubMed ID: 37158609
    [TBL] [Abstract][Full Text] [Related]  

  • 13. NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.
    Shen ZA; Luo T; Zhou YK; Yu H; Du PF
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33822882
    [TBL] [Abstract][Full Text] [Related]  

  • 14. RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.
    Yi HC; You ZH; Wang MN; Guo ZH; Wang YB; Zhou JR
    BMC Bioinformatics; 2020 Feb; 21(1):60. PubMed ID: 32070279
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.
    Wang L; You ZH; Chen X; Xia SX; Liu F; Yan X; Zhou Y; Song KJ
    J Comput Biol; 2018 Mar; 25(3):361-373. PubMed ID: 28891684
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of the RBP binding sites on lncRNAs using the high-order nucleotide encoding convolutional neural network.
    Zhang SW; Wang Y; Zhang XX; Wang JQ
    Anal Biochem; 2019 Oct; 583():113364. PubMed ID: 31323206
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier.
    Cao Z; Pan X; Yang Y; Huang Y; Shen HB
    Bioinformatics; 2018 Jul; 34(13):2185-2194. PubMed ID: 29462250
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants.
    Chao H; Hu Y; Zhao L; Xin S; Ni Q; Zhang P; Chen M
    Int J Mol Sci; 2022 Mar; 23(7):. PubMed ID: 35409060
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Convolutional neural networks for classification of alignments of non-coding RNA sequences.
    Aoki G; Sakakibara Y
    Bioinformatics; 2018 Jul; 34(13):i237-i244. PubMed ID: 29949978
    [TBL] [Abstract][Full Text] [Related]  

  • 20. MFPINC: prediction of plant ncRNAs based on multi-source feature fusion.
    Nie Z; Gao M; Jin X; Rao Y; Zhang X
    BMC Genomics; 2024 May; 25(1):531. PubMed ID: 38816689
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.