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 *

191 related articles for article (PubMed ID: 32145017)

  • 1. HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation.
    Hasan MM; Schaduangrat N; Basith S; Lee G; Shoombuatong W; Manavalan B
    Bioinformatics; 2020 Jun; 36(11):3350-3356. PubMed ID: 32145017
    [TBL] [Abstract][Full Text] [Related]  

  • 2. ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides.
    Rao B; Zhou C; Zhang G; Su R; Wei L
    Brief Bioinform; 2020 Sep; 21(5):1846-1855. PubMed ID: 31729528
    [TBL] [Abstract][Full Text] [Related]  

  • 3. ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations.
    Khatun MS; Hasan MM; Shoombuatong W; Kurata H
    J Comput Aided Mol Des; 2020 Dec; 34(12):1229-1236. PubMed ID: 32964284
    [TBL] [Abstract][Full Text] [Related]  

  • 4. mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation.
    Manavalan B; Basith S; Shin TH; Wei L; Lee G
    Bioinformatics; 2019 Aug; 35(16):2757-2765. PubMed ID: 30590410
    [TBL] [Abstract][Full Text] [Related]  

  • 5. iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.
    Charoenkwan P; Nantasenamat C; Hasan MM; Moni MA; Lio' P; Shoombuatong W
    Int J Mol Sci; 2021 Aug; 22(16):. PubMed ID: 34445663
    [TBL] [Abstract][Full Text] [Related]  

  • 6. PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning.
    Wei L; Zhou C; Su R; Zou Q
    Bioinformatics; 2019 Nov; 35(21):4272-4280. PubMed ID: 30994882
    [TBL] [Abstract][Full Text] [Related]  

  • 7. UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.
    Charoenkwan P; Nantasenamat C; Hasan MM; Moni MA; Manavalan B; Shoombuatong W
    Int J Mol Sci; 2021 Dec; 22(23):. PubMed ID: 34884927
    [TBL] [Abstract][Full Text] [Related]  

  • 8. IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.
    Hasan MM; Alam MA; Shoombuatong W; Kurata H
    J Comput Aided Mol Des; 2021 Mar; 35(3):315-323. PubMed ID: 33392948
    [TBL] [Abstract][Full Text] [Related]  

  • 9. ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information.
    Basith S; Pham NT; Song M; Lee G; Manavalan B
    Comput Biol Med; 2023 Oct; 165():107386. PubMed ID: 37619323
    [TBL] [Abstract][Full Text] [Related]  

  • 10. PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.
    Zhang Y; Yu S; Xie R; Li J; Leier A; Marquez-Lago TT; Akutsu T; Smith AI; Ge Z; Wang J; Lithgow T; Song J
    Bioinformatics; 2020 Feb; 36(3):704-712. PubMed ID: 31393553
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy.
    Manavalan B; Subramaniyam S; Shin TH; Kim MO; Lee G
    J Proteome Res; 2018 Aug; 17(8):2715-2726. PubMed ID: 29893128
    [TBL] [Abstract][Full Text] [Related]  

  • 12. ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.
    Wei L; Ye X; Sakurai T; Mu Z; Wei L
    Bioinformatics; 2022 Mar; 38(6):1514-1524. PubMed ID: 34999757
    [TBL] [Abstract][Full Text] [Related]  

  • 13. AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees.
    Manavalan B; Basith S; Shin TH; Wei L; Lee G
    Comput Struct Biotechnol J; 2019; 17():972-981. PubMed ID: 31372196
    [No Abstract]   [Full Text] [Related]  

  • 14. mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.
    Boopathi V; Subramaniyam S; Malik A; Lee G; Manavalan B; Yang DC
    Int J Mol Sci; 2019 Apr; 20(8):. PubMed ID: 31013619
    [TBL] [Abstract][Full Text] [Related]  

  • 15. HemoNet: Predicting hemolytic activity of peptides with integrated feature learning.
    Yaseen A; Gull S; Akhtar N; Amin I; Minhas F
    J Bioinform Comput Biol; 2021 Oct; 19(5):2150021. PubMed ID: 34353244
    [TBL] [Abstract][Full Text] [Related]  

  • 16. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides.
    Wei L; Zhou C; Chen H; Song J; Su R
    Bioinformatics; 2018 Dec; 34(23):4007-4016. PubMed ID: 29868903
    [TBL] [Abstract][Full Text] [Related]  

  • 17. PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning.
    Zhang YP; Zou Q
    Bioinformatics; 2020 Jul; 36(13):3982-3987. PubMed ID: 32348463
    [TBL] [Abstract][Full Text] [Related]  

  • 18. NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.
    Hasan MM; Alam MA; Shoombuatong W; Deng HW; Manavalan B; Kurata H
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 33975333
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Enhancer-FRL: Improved and Robust Identification of Enhancers and Their Activities Using Feature Representation Learning.
    Wang C; Zou Q; Ju Y; Shi H
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):967-975. PubMed ID: 36063523
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.
    Wei L; Luan S; Nagai LAE; Su R; Zou Q
    Bioinformatics; 2019 Apr; 35(8):1326-1333. PubMed ID: 30239627
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.