BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

545 related articles for article (PubMed ID: 23889050)

  • 1. General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry.
    González-Díaz H; Arrasate S; Gómez-SanJuan A; Sotomayor N; Lete E; Besada-Porto L; Ruso JM
    Curr Top Med Chem; 2013; 13(14):1713-41. PubMed ID: 23889050
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions, micelle nanoparticles, and drug ADME processes.
    Gonzalez-Diaz H; Arrasate S; Juan AG; Sotomayor N; Lete E; Speck-Planche A; Ruso JM; Luan F; Cordeiro MN
    Curr Drug Metab; 2014; 15(4):470-88. PubMed ID: 25204825
    [TBL] [Abstract][Full Text] [Related]  

  • 3. From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices.
    Riera-Fernández P; Martín-Romalde R; Prado-Prado FJ; Escobar M; Munteanu CR; Concu R; Duardo-Sanchez A; González-Díaz H
    Curr Top Med Chem; 2012; 12(8):927-60. PubMed ID: 22352918
    [TBL] [Abstract][Full Text] [Related]  

  • 4. PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer.
    Bediaga H; Arrasate S; González-Díaz H
    ACS Comb Sci; 2018 Nov; 20(11):621-632. PubMed ID: 30240186
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.
    Ferreira da Costa J; Silva D; Caamaño O; Brea JM; Loza MI; Munteanu CR; Pazos A; García-Mera X; González-Díaz H
    ACS Chem Neurosci; 2018 Nov; 9(11):2572-2587. PubMed ID: 29791132
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers.
    González-Díaz H; Duardo-Sanchez A; Ubeira FM; Prado-Prado F; Pérez-Montoto LG; Concu R; Podda G; Shen B
    Curr Drug Metab; 2010 May; 11(4):379-406. PubMed ID: 20446904
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches.
    Chen M; Yang J; Tang C; Lu X; Wei Z; Liu Y; Yu P; Li H
    Curr Top Med Chem; 2024; 24(3):222-242. PubMed ID: 38083894
    [TBL] [Abstract][Full Text] [Related]  

  • 8. QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemical compounds with immune cellular and molecular targets.
    Tenorio-Borroto E; Ramirez FR; Speck-Planche A; Cordeiro MN; Luan F; Gonzalez-Diaz H
    Curr Drug Metab; 2014; 15(4):414-28. PubMed ID: 25204826
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.
    Simón-Vidal L; García-Calvo O; Oteo U; Arrasate S; Lete E; Sotomayor N; González-Díaz H
    J Chem Inf Model; 2018 Jul; 58(7):1384-1396. PubMed ID: 29898360
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Galvez-Markov network transferability indices: review of classic theory and new model for perturbations in metabolic reactions.
    Vergara-Galicia J; Prado-Prado FJ; Gonzalez-Diaz H
    Curr Drug Metab; 2014; 15(5):557-64. PubMed ID: 24909421
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models.
    Santana R; Zuluaga R; Gañán P; Arrasate S; Onieva E; González-Díaz H
    Nanoscale; 2020 Jul; 12(25):13471-13483. PubMed ID: 32613998
    [TBL] [Abstract][Full Text] [Related]  

  • 12. ADMET-Multi-Output Cheminformatics Models for Drug Delivery, Interactomics, and Nanotoxicology.
    González-Díaz H
    Curr Drug Deliv; 2016 Apr; ():. PubMed ID: 27417300
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental-Computational Study of NaGDC-DDAB Micelles.
    Messina PV; Besada-Porto JM; González-Díaz H; Ruso JM
    Langmuir; 2015 Nov; 31(44):12009-18. PubMed ID: 26484726
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms.
    González-Díaz H; Pérez-Montoto LG; Ubeira FM
    J Immunol Res; 2014; 2014():768515. PubMed ID: 24741624
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Perturbation Theory Machine Learning Models: Theory, Regulatory Issues, and Applications to Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology.
    Arrasate S; Duardo-Sanchez A
    Curr Top Med Chem; 2018; 18(14):1203-1213. PubMed ID: 30095052
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models.
    Santana R; Zuluaga R; Gañán P; Arrasate S; Onieva E; González-Díaz H
    Nanoscale; 2019 Nov; 11(45):21811-21823. PubMed ID: 31691701
    [TBL] [Abstract][Full Text] [Related]  

  • 17. IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.
    Quevedo-Tumailli V; Ortega-Tenezaca B; González-Díaz H
    Int J Mol Sci; 2021 Dec; 22(23):. PubMed ID: 34884870
    [TBL] [Abstract][Full Text] [Related]  

  • 18. QSPR models for various physical properties of carbohydrates based on molecular mechanics and quantum chemical calculations.
    Dyekjaer JD; Jónsdóttir SO
    Carbohydr Res; 2004 Jan; 339(2):269-80. PubMed ID: 14698885
    [TBL] [Abstract][Full Text] [Related]  

  • 19. How Does the Methodology of 3D Structure Preparation Influence the Quality of pKa Prediction?
    Geidl S; Svobodová Vařeková R; Bendová V; Petrusek L; Ionescu CM; Jurka Z; Abagyan R; Koča J
    J Chem Inf Model; 2015 Jun; 55(6):1088-97. PubMed ID: 26010215
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting pK(a) values of substituted phenols from atomic charges: comparison of different quantum mechanical methods and charge distribution schemes.
    Svobodová Vareková R; Geidl S; Ionescu CM; Skrehota O; Kudera M; Sehnal D; Bouchal T; Abagyan R; Huber HJ; Koca J
    J Chem Inf Model; 2011 Aug; 51(8):1795-806. PubMed ID: 21761919
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 28.