BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

482 related articles for article (PubMed ID: 25635324)

  • 1. Deep neural nets as a method for quantitative structure-activity relationships.
    Ma J; Sheridan RP; Liaw A; Dahl GE; Svetnik V
    J Chem Inf Model; 2015 Feb; 55(2):263-74. PubMed ID: 25635324
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.
    Xu Y; Ma J; Liaw A; Sheridan RP; Svetnik V
    J Chem Inf Model; 2017 Oct; 57(10):2490-2504. PubMed ID: 28872869
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.
    Lin WQ; Jiang JH; Zhou YP; Wu HL; Shen GL; Yu RQ
    J Comput Chem; 2007 Jan; 28(2):519-27. PubMed ID: 17186488
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships.
    Sheridan RP; Wang WM; Liaw A; Ma J; Gifford EM
    J Chem Inf Model; 2016 Dec; 56(12):2353-2360. PubMed ID: 27958738
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure-Activity Relationship Models Based on Deep Neural Networks?
    Liu R; Wang H; Glover KP; Feasel MG; Wallqvist A
    J Chem Inf Model; 2019 Jan; 59(1):117-126. PubMed ID: 30412667
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets.
    Cortes-Ciriano I; Bender A; Malliavin TE
    J Chem Inf Model; 2015 Jul; 55(7):1413-25. PubMed ID: 26038978
    [TBL] [Abstract][Full Text] [Related]  

  • 7. GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework.
    Deng L; Jiao P; Pei J; Wu Z; Li G
    Neural Netw; 2018 Apr; 100():49-58. PubMed ID: 29471195
    [TBL] [Abstract][Full Text] [Related]  

  • 8. ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.
    Lei T; Sun H; Kang Y; Zhu F; Liu H; Zhou W; Wang Z; Li D; Li Y; Hou T
    Mol Pharm; 2017 Nov; 14(11):3935-3953. PubMed ID: 29037046
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Application of support vector machine (SVM) for prediction toxic activity of different data sets.
    Zhao CY; Zhang HX; Zhang XY; Liu MC; Hu ZD; Fan BT
    Toxicology; 2006 Jan; 217(2-3):105-19. PubMed ID: 16213080
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.
    Winkler DA; Le TC
    Mol Inform; 2017 Jan; 36(1-2):. PubMed ID: 27783464
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Relevance Vector Machines: Sparse Classification Methods for QSAR.
    Burden FR; Winkler DA
    J Chem Inf Model; 2015 Aug; 55(8):1529-34. PubMed ID: 26158341
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Choosing feature selection and learning algorithms in QSAR.
    Eklund M; Norinder U; Boyer S; Carlsson L
    J Chem Inf Model; 2014 Mar; 54(3):837-43. PubMed ID: 24460242
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.
    Pastur-Romay LA; Cedrón F; Pazos A; Porto-Pazos AB
    Int J Mol Sci; 2016 Aug; 17(8):. PubMed ID: 27529225
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Molecular Similarity-Based Domain Applicability Metric Efficiently Identifies Out-of-Domain Compounds.
    Liu R; Wallqvist A
    J Chem Inf Model; 2019 Jan; 59(1):181-189. PubMed ID: 30404432
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine Learning Methods in Computational Toxicology.
    Baskin II
    Methods Mol Biol; 2018; 1800():119-139. PubMed ID: 29934890
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.
    Ventura C; Latino DA; Martins F
    Eur J Med Chem; 2013; 70():831-45. PubMed ID: 24246731
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The role of different sampling methods in improving biological activity prediction using deep belief network.
    Ghasemi F; Fassihi A; Pérez-Sánchez H; Mehri Dehnavi A
    J Comput Chem; 2017 Feb; 38(4):195-203. PubMed ID: 27862046
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.
    Liu R; Schyman P; Wallqvist A
    J Chem Inf Model; 2015 Aug; 55(8):1566-75. PubMed ID: 26170251
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data.
    Koutsoukas A; Monaghan KJ; Li X; Huan J
    J Cheminform; 2017 Jun; 9(1):42. PubMed ID: 29086090
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure-activity relationship modeling.
    Bouhedjar K; Boukelia A; Khorief Nacereddine A; Boucheham A; Belaidi A; Djerourou A
    Chem Biol Drug Des; 2020 Sep; 96(3):961-972. PubMed ID: 33058460
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 25.