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 *

471 related articles for article (PubMed ID: 31415569)

  • 41. DeepSol: a deep learning framework for sequence-based protein solubility prediction.
    Khurana S; Rawi R; Kunji K; Chuang GY; Bensmail H; Mall R
    Bioinformatics; 2018 Aug; 34(15):2605-2613. PubMed ID: 29554211
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Amino acid encoding for deep learning applications.
    ElAbd H; Bromberg Y; Hoarfrost A; Lenz T; Franke A; Wendorff M
    BMC Bioinformatics; 2020 Jun; 21(1):235. PubMed ID: 32517697
    [TBL] [Abstract][Full Text] [Related]  

  • 43. NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.
    Klausen MS; Jespersen MC; Nielsen H; Jensen KK; Jurtz VI; Sønderby CK; Sommer MOA; Winther O; Nielsen M; Petersen B; Marcatili P
    Proteins; 2019 Jun; 87(6):520-527. PubMed ID: 30785653
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Efficient utilization on PSSM combining with recurrent neural network for membrane protein types prediction.
    Wang S; Li M; Guo L; Cao Z; Fei Y
    Comput Biol Chem; 2019 Aug; 81():9-15. PubMed ID: 31472418
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks.
    Liu Y; Palmedo P; Ye Q; Berger B; Peng J
    Cell Syst; 2018 Jan; 6(1):65-74.e3. PubMed ID: 29275173
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Recent developments in deep learning applied to protein structure prediction.
    Kandathil SM; Greener JG; Jones DT
    Proteins; 2019 Dec; 87(12):1179-1189. PubMed ID: 31589782
    [TBL] [Abstract][Full Text] [Related]  

  • 47. Pairing interacting protein sequences using masked language modeling.
    Lupo U; Sgarbossa D; Bitbol AF
    Proc Natl Acad Sci U S A; 2024 Jul; 121(27):e2311887121. PubMed ID: 38913900
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Lightweight ProteinUnet2 network for protein secondary structure prediction: a step towards proper evaluation.
    Stapor K; Kotowski K; Smolarczyk T; Roterman I
    BMC Bioinformatics; 2022 Mar; 23(1):100. PubMed ID: 35317722
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks.
    Guo Y; Wang B; Li W; Yang B
    J Bioinform Comput Biol; 2018 Oct; 16(5):1850021. PubMed ID: 30419785
    [TBL] [Abstract][Full Text] [Related]  

  • 50. DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning.
    Quadir F; Roy RS; Halfmann R; Cheng J
    Sci Rep; 2021 Jun; 11(1):12295. PubMed ID: 34112907
    [TBL] [Abstract][Full Text] [Related]  

  • 51. DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network.
    Chen X; Cheng J
    BMC Bioinformatics; 2022 Apr; 23(Suppl 3):141. PubMed ID: 35439931
    [TBL] [Abstract][Full Text] [Related]  

  • 52. EPTool: A New Enhancing PSSM Tool for Protein Secondary Structure Prediction.
    Guo Y; Wu J; Ma H; Wang S; Huang J
    J Comput Biol; 2021 Apr; 28(4):362-364. PubMed ID: 33259717
    [No Abstract]   [Full Text] [Related]  

  • 53. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences.
    Tsubaki M; Tomii K; Sese J
    Bioinformatics; 2019 Jan; 35(2):309-318. PubMed ID: 29982330
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Modeling aspects of the language of life through transfer-learning protein sequences.
    Heinzinger M; Elnaggar A; Wang Y; Dallago C; Nechaev D; Matthes F; Rost B
    BMC Bioinformatics; 2019 Dec; 20(1):723. PubMed ID: 31847804
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction.
    Jain A; Terashi G; Kagaya Y; Maddhuri Venkata Subramaniya SR; Christoffer C; Kihara D
    Sci Rep; 2021 Apr; 11(1):7574. PubMed ID: 33828153
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.
    Suh D; Lee JW; Choi S; Lee Y
    Int J Mol Sci; 2021 Jun; 22(11):. PubMed ID: 34199677
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Flattening the curve-How to get better results with small deep-mutational-scanning datasets.
    Wirnsberger G; Pritišanac I; Oberdorfer G; Gruber K
    Proteins; 2024 Jul; 92(7):886-902. PubMed ID: 38501649
    [TBL] [Abstract][Full Text] [Related]  

  • 58. IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction.
    Görmez Y; Sabzekar M; Aydın Z
    Proteins; 2021 Oct; 89(10):1277-1288. PubMed ID: 33993559
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning.
    Heffernan R; Paliwal K; Lyons J; Singh J; Yang Y; Zhou Y
    J Comput Chem; 2018 Oct; 39(26):2210-2216. PubMed ID: 30368831
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.
    Du T; Liao L; Wu CH; Sun B
    Methods; 2016 Nov; 110():97-105. PubMed ID: 27282356
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 24.