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 *

359 related articles for article (PubMed ID: 24723570)

  • 1. Toward more realistic drug-target interaction predictions.
    Pahikkala T; Airola A; Pietilä S; Shakyawar S; Szwajda A; Tang J; Aittokallio T
    Brief Bioinform; 2015 Mar; 16(2):325-37. PubMed ID: 24723570
    [TBL] [Abstract][Full Text] [Related]  

  • 2. BE-DTI': Ensemble framework for drug target interaction prediction using dimensionality reduction and active learning.
    Sharma A; Rani R
    Comput Methods Programs Biomed; 2018 Oct; 165():151-162. PubMed ID: 30337070
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Drug-target interaction prediction: databases, web servers and computational models.
    Chen X; Yan CC; Zhang X; Zhang X; Dai F; Yin J; Zhang Y
    Brief Bioinform; 2016 Jul; 17(4):696-712. PubMed ID: 26283676
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model.
    Le DH; Nguyen-Ngoc D
    Acta Biotheor; 2018 Dec; 66(4):315-331. PubMed ID: 29700660
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.
    Tanoori B; Zolghadri Jahromi M; Mansoori EG
    J Comput Aided Mol Des; 2021 Aug; 35(8):883-900. PubMed ID: 34189637
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.
    Cichonska A; Ravikumar B; Parri E; Timonen S; Pahikkala T; Airola A; Wennerberg K; Rousu J; Aittokallio T
    PLoS Comput Biol; 2017 Aug; 13(8):e1005678. PubMed ID: 28787438
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.
    Nath A; Kumari P; Chaube R
    Methods Mol Biol; 2018; 1762():21-30. PubMed ID: 29594765
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources.
    Nascimento ACA; Prudêncio RBC; Costa IG
    Methods Mol Biol; 2019; 1903():281-289. PubMed ID: 30547449
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Computational Prediction of Drug-Target Interactions via Ensemble Learning.
    Ezzat A; Wu M; Li X; Kwoh CK
    Methods Mol Biol; 2019; 1903():239-254. PubMed ID: 30547446
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.
    Redkar S; Mondal S; Joseph A; Hareesha KS
    Mol Inform; 2020 May; 39(5):e1900062. PubMed ID: 32003548
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Identification of drug candidates and repurposing opportunities through compound-target interaction networks.
    Cichonska A; Rousu J; Aittokallio T
    Expert Opin Drug Discov; 2015 Dec; 10(12):1333-45. PubMed ID: 26429153
    [TBL] [Abstract][Full Text] [Related]  

  • 13. REPRODUCIBLE DRUG REPURPOSING: WHEN SIMILARITY DOES NOT SUFFICE.
    Guney E
    Pac Symp Biocomput; 2017; 22():132-143. PubMed ID: 27896969
    [TBL] [Abstract][Full Text] [Related]  

  • 14. How to approach machine learning-based prediction of drug/compound-target interactions.
    Atas Guvenilir H; Doğan T
    J Cheminform; 2023 Feb; 15(1):16. PubMed ID: 36747300
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network.
    Yan XY; Zhang SW; Zhang SY
    Mol Biosyst; 2016 Feb; 12(2):520-31. PubMed ID: 26675534
    [TBL] [Abstract][Full Text] [Related]  

  • 17. DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.
    Yuan Q; Gao J; Wu D; Zhang S; Mamitsuka H; Zhu S
    Bioinformatics; 2016 Jun; 32(12):i18-i27. PubMed ID: 27307615
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting drug-target interactions using restricted Boltzmann machines.
    Wang Y; Zeng J
    Bioinformatics; 2013 Jul; 29(13):i126-34. PubMed ID: 23812976
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting protein targets for drug-like compounds using transcriptomics.
    Pabon NA; Xia Y; Estabrooks SK; Ye Z; Herbrand AK; Süß E; Biondi RM; Assimon VA; Gestwicki JE; Brodsky JL; Camacho CJ; Bar-Joseph Z
    PLoS Comput Biol; 2018 Dec; 14(12):e1006651. PubMed ID: 30532261
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Training based on ligand efficiency improves prediction of bioactivities of ligands and drug target proteins in a machine learning approach.
    Sugaya N
    J Chem Inf Model; 2013 Oct; 53(10):2525-37. PubMed ID: 24020509
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.