BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

191 related articles for article (PubMed ID: 32309431)

  • 21. 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]  

  • 22. Helix-helix packing and interfacial pairwise interactions of residues in membrane proteins.
    Adamian L; Liang J
    J Mol Biol; 2001 Aug; 311(4):891-907. PubMed ID: 11518538
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Accurate prediction of helix interactions and residue contacts in membrane proteins.
    Hönigschmid P; Frishman D
    J Struct Biol; 2016 Apr; 194(1):112-23. PubMed ID: 26851352
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Predicting helix-helix interactions from residue contacts in membrane proteins.
    Lo A; Chiu YY; Rødland EA; Lyu PC; Sung TY; Hsu WL
    Bioinformatics; 2009 Apr; 25(8):996-1003. PubMed ID: 19244388
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Protein Residue Contacts and Prediction Methods.
    Adhikari B; Cheng J
    Methods Mol Biol; 2016; 1415():463-76. PubMed ID: 27115648
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Predicting the helix-helix interactions from correlated residue mutations.
    Xiong D; Mao W; Gong H
    Proteins; 2017 Dec; 85(12):2162-2169. PubMed ID: 28833538
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Predicting Alpha Helical Transmembrane Proteins Using HMMs.
    Tsaousis GN; Theodoropoulou MC; Hamodrakas SJ; Bagos PG
    Methods Mol Biol; 2017; 1552():63-82. PubMed ID: 28224491
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties.
    Petrova NV; Wu CH
    BMC Bioinformatics; 2006 Jun; 7():312. PubMed ID: 16790052
    [TBL] [Abstract][Full Text] [Related]  

  • 29. DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.
    Adhikari B; Hou J; Cheng J
    Bioinformatics; 2018 May; 34(9):1466-1472. PubMed ID: 29228185
    [TBL] [Abstract][Full Text] [Related]  

  • 30. RRCRank: a fusion method using rank strategy for residue-residue contact prediction.
    Jing X; Dong Q; Lu R
    BMC Bioinformatics; 2017 Sep; 18(1):390. PubMed ID: 28865433
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks.
    Kukic P; Mirabello C; Tradigo G; Walsh I; Veltri P; Pollastri G
    BMC Bioinformatics; 2014 Jan; 15():6. PubMed ID: 24410833
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Detecting distant-homology protein structures by aligning deep neural-network based contact maps.
    Zheng W; Wuyun Q; Li Y; Mortuza SM; Zhang C; Pearce R; Ruan J; Zhang Y
    PLoS Comput Biol; 2019 Oct; 15(10):e1007411. PubMed ID: 31622328
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Predicting protein residue-residue contacts using random forests and deep networks.
    Luttrell J; Liu T; Zhang C; Wang Z
    BMC Bioinformatics; 2019 Mar; 20(Suppl 2):100. PubMed ID: 30871477
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network.
    Nguyen TT; Le NQ; Kusuma RMI; Ou YY
    J Mol Graph Model; 2019 Nov; 92():86-93. PubMed ID: 31344547
    [TBL] [Abstract][Full Text] [Related]  

  • 35. A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.
    Li B; Mendenhall J; Capra JA; Meiler J
    J Proteome Res; 2021 Aug; 20(8):4089-4100. PubMed ID: 34236204
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks.
    Feng SH; Xia CQ; Zhang PD; Shen HB
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(2):795-805. PubMed ID: 33026978
    [TBL] [Abstract][Full Text] [Related]  

  • 37. AllesTM: predicting multiple structural features of transmembrane proteins.
    Hönigschmid P; Breimann S; Weigl M; Frishman D
    BMC Bioinformatics; 2020 Jun; 21(1):242. PubMed ID: 32532211
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Cascading classifier application for topology prediction of transmembrane beta-barrel proteins.
    Kazemian HB; Grimaldi CM
    J Bioinform Comput Biol; 2020 Dec; 18(6):2050034. PubMed ID: 33064051
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Predicting Beta Barrel Transmembrane Proteins Using HMMs.
    Tsaousis GN; Hamodrakas SJ; Bagos PG
    Methods Mol Biol; 2017; 1552():43-61. PubMed ID: 28224490
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Deep Conditional Random Field Approach to Transmembrane Topology Prediction and Application to GPCR Three-Dimensional Structure Modeling.
    Wu H; Wang K; Lu L; Xue Y; Lyu Q; Jiang M
    IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(5):1106-1114. PubMed ID: 27576262
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.