BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

330 related articles for article (PubMed ID: 32173600)

  • 21. SPOT-1D-Single: improving the single-sequence-based prediction of protein secondary structure, backbone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning.
    Singh J; Litfin T; Paliwal K; Singh J; Hanumanthappa AK; Zhou Y
    Bioinformatics; 2021 Oct; 37(20):3464-3472. PubMed ID: 33983382
    [TBL] [Abstract][Full Text] [Related]  

  • 22. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.
    Deng X; Gumm J; Karki S; Eickholt J; Cheng J
    Int J Mol Sci; 2015 Jul; 16(7):15384-404. PubMed ID: 26198229
    [TBL] [Abstract][Full Text] [Related]  

  • 23. cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks.
    Oberti M; Vaisman II
    Proteins; 2020 Nov; 88(11):1472-1481. PubMed ID: 32535960
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Improved protein relative solvent accessibility prediction using deep multi-view feature learning framework.
    Fan XQ; Hu J; Jia NX; Yu DJ; Zhang GJ
    Anal Biochem; 2021 Oct; 631():114358. PubMed ID: 34478704
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions.
    Saravanan KM; Dunker AK; Krishnaswamy S
    J Biomol Struct Dyn; 2018 Dec; 36(16):4338-4351. PubMed ID: 29228892
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Protein embeddings predict binding residues in disordered regions.
    Jahn LR; Marquet C; Heinzinger M; Rost B
    Sci Rep; 2024 Jun; 14(1):13566. PubMed ID: 38866950
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Computational identification of MoRFs in protein sequences.
    Malhis N; Gsponer J
    Bioinformatics; 2015 Jun; 31(11):1738-44. PubMed ID: 25637562
    [TBL] [Abstract][Full Text] [Related]  

  • 28. A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction.
    Liu Y; Wang X; Liu B
    Brief Bioinform; 2019 Jan; 20(1):330-346. PubMed ID: 30657889
    [TBL] [Abstract][Full Text] [Related]  

  • 29. A Comprehensive Survey of the Roles of Highly Disordered Proteins in Type 2 Diabetes.
    Du Z; Uversky VN
    Int J Mol Sci; 2017 Sep; 18(10):. PubMed ID: 28934129
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Molecular Recognition Features in Zika Virus Proteome.
    Mishra PM; Uversky VN; Giri R
    J Mol Biol; 2018 Aug; 430(16):2372-2388. PubMed ID: 29080786
    [TBL] [Abstract][Full Text] [Related]  

  • 31. DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning.
    Zhang F; Zhao B; Shi W; Li M; Kurgan L
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34905768
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Intrinsic disorder mediates hepatitis C virus core-host cell protein interactions.
    Dolan PT; Roth AP; Xue B; Sun R; Dunker AK; Uversky VN; LaCount DJ
    Protein Sci; 2015 Feb; 24(2):221-35. PubMed ID: 25424537
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks.
    Hanson J; Paliwal K; Litfin T; Yang Y; Zhou Y
    Bioinformatics; 2019 Jul; 35(14):2403-2410. PubMed ID: 30535134
    [TBL] [Abstract][Full Text] [Related]  

  • 34. The contribution of intrinsic disorder prediction to the elucidation of protein function.
    Cozzetto D; Jones DT
    Curr Opin Struct Biol; 2013 Jun; 23(3):467-72. PubMed ID: 23466039
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.
    Heffernan R; Yang Y; Paliwal K; Zhou Y
    Bioinformatics; 2017 Sep; 33(18):2842-2849. PubMed ID: 28430949
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Abundance of intrinsic structural disorder in the histone H1 subtypes.
    Kowalski A
    Comput Biol Chem; 2015 Dec; 59 Pt A():16-27. PubMed ID: 26366527
    [TBL] [Abstract][Full Text] [Related]  

  • 37. MemDis: Predicting Disordered Regions in Transmembrane Proteins.
    Dobson L; Tusnády GE
    Int J Mol Sci; 2021 Nov; 22(22):. PubMed ID: 34830151
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.
    Singh J; Paliwal K; Litfin T; Singh J; Zhou Y
    Bioinformatics; 2022 Aug; 38(16):3900-3910. PubMed ID: 35751593
    [TBL] [Abstract][Full Text] [Related]  

  • 39. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.
    Wang Z; Yang Q; Li T; Cong P
    PLoS One; 2015; 10(6):e0128334. PubMed ID: 26090958
    [TBL] [Abstract][Full Text] [Related]  

  • 40. OPAL+: Length-Specific MoRF Prediction in Intrinsically Disordered Protein Sequences.
    Sharma R; Sharma A; Raicar G; Tsunoda T; Patil A
    Proteomics; 2019 Mar; 19(6):e1800058. PubMed ID: 30324701
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 17.