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 *

224 related articles for article (PubMed ID: 31357419)

  • 1. Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.
    Polash AH; Nakano T; Takeda S; Brown JB
    Molecules; 2019 Jul; 24(15):. PubMed ID: 31357419
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Active learning for computational chemogenomics.
    Reker D; Schneider P; Schneider G; Brown JB
    Future Med Chem; 2017 Mar; 9(4):381-402. PubMed ID: 28263088
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families.
    Rakers C; Najnin RA; Polash AH; Takeda S; Brown JB
    ChemMedChem; 2018 Mar; 13(6):511-521. PubMed ID: 29211346
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automated Inference of Chemical Discriminants of Biological Activity.
    Raschka S; Scott AM; Huertas M; Li W; Kuhn LA
    Methods Mol Biol; 2018; 1762():307-338. PubMed ID: 29594779
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Linear and Kernel Model Construction Methods for Predicting Drug-Target Interactions in a Chemogenomic Framework.
    Yamanishi Y
    Methods Mol Biol; 2018; 1825():355-368. PubMed ID: 30334213
    [TBL] [Abstract][Full Text] [Related]  

  • 6. TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database.
    Wang L; Ma C; Wipf P; Liu H; Su W; Xie XQ
    AAPS J; 2013 Apr; 15(2):395-406. PubMed ID: 23292636
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches.
    Li B; Hu L; Xue Y; Yang M; Huang L; Zhang Z; Liu J; Deng G
    J Biomol Struct Dyn; 2019 Jul; 37(10):2627-2640. PubMed ID: 30051748
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Selection of Informative Examples in Chemogenomic Datasets.
    Reker D; Brown JB
    Methods Mol Biol; 2018; 1825():369-410. PubMed ID: 30334214
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.
    Liu X; Feng H; Wu J; Xia K
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33837771
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method.
    Wei Y; Li W; Du T; Hong Z; Lin J
    Int J Mol Sci; 2019 Jul; 20(14):. PubMed ID: 31336592
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting kinase inhibitors using bioactivity matrix derived informer sets.
    Zhang H; Ericksen SS; Lee CP; Ananiev GE; Wlodarchak N; Yu P; Mitchell JC; Gitter A; Wright SJ; Hoffmann FM; Wildman SA; Newton MA
    PLoS Comput Biol; 2019 Aug; 15(8):e1006813. PubMed ID: 31381559
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Comparative Analysis of QSAR-based vs. Chemical Similarity Based Predictors of GPCRs Binding Affinity.
    Luo M; Wang XS; Tropsha A
    Mol Inform; 2016 Jan; 35(1):36-41. PubMed ID: 27491652
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Computational chemogenomics: is it more than inductive transfer?
    Brown JB; Okuno Y; Marcou G; Varnek A; Horvath D
    J Comput Aided Mol Des; 2014 Jun; 28(6):597-618. PubMed ID: 24771144
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Use of machine learning approaches for novel drug discovery.
    Lima AN; Philot EA; Trossini GH; Scott LP; Maltarollo VG; Honorio KM
    Expert Opin Drug Discov; 2016; 11(3):225-39. PubMed ID: 26814169
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Extremely Randomized Machine Learning Methods for Compound Activity Prediction.
    Czarnecki WM; Podlewska S; Bojarski AJ
    Molecules; 2015 Nov; 20(11):20107-17. PubMed ID: 26569196
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits.
    Ihmaid SK; Ahmed HE; Zayed MF; Abadleh MM
    Molecules; 2016 Jan; 21(2):175. PubMed ID: 26840291
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Boosted feature selectors: a case study on prediction P-gp inhibitors and substrates.
    Cerruela García G; García-Pedrajas N
    J Comput Aided Mol Des; 2018 Nov; 32(11):1273-1294. PubMed ID: 30367310
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database.
    Butkiewicz M; Lowe EW; Mueller R; Mendenhall JL; Teixeira PL; Weaver CD; Meiler J
    Molecules; 2013 Jan; 18(1):735-56. PubMed ID: 23299552
    [TBL] [Abstract][Full Text] [Related]  

  • 19. The Future of Computational Chemogenomics.
    Jacoby E; Brown JB
    Methods Mol Biol; 2018; 1825():425-450. PubMed ID: 30334216
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prediction of protein-ligand interactions from paired protein sequence motifs and ligand substructures.
    Greenside P; Hillenmeyer M; Kundaje A
    Pac Symp Biocomput; 2018; 23():20-31. PubMed ID: 29218866
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.