BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1049 related articles for article (PubMed ID: 24279462)

  • 1. Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.
    Zang Q; Rotroff DM; Judson RS
    J Chem Inf Model; 2013 Dec; 53(12):3244-61. PubMed ID: 24279462
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.
    Zhang L; Sedykh A; Tripathi A; Zhu H; Afantitis A; Mouchlis VD; Melagraki G; Rusyn I; Tropsha A
    Toxicol Appl Pharmacol; 2013 Oct; 272(1):67-76. PubMed ID: 23707773
    [TBL] [Abstract][Full Text] [Related]  

  • 3. In silico screening of estrogen-like chemicals based on different nonlinear classification models.
    Liu H; Papa E; Walker JD; Gramatica P
    J Mol Graph Model; 2007 Jul; 26(1):135-44. PubMed ID: 17293141
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure.
    Liu J; Mansouri K; Judson RS; Martin MT; Hong H; Chen M; Xu X; Thomas RS; Shah I
    Chem Res Toxicol; 2015 Apr; 28(4):738-51. PubMed ID: 25697799
    [TBL] [Abstract][Full Text] [Related]  

  • 5. In Silico Study of In Vitro GPCR Assays by QSAR Modeling.
    Mansouri K; Judson RS
    Methods Mol Biol; 2016; 1425():361-81. PubMed ID: 27311474
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A ternary classification using machine learning methods of distinct estrogen receptor activities within a large collection of environmental chemicals.
    Zhang Q; Yan L; Wu Y; Ji L; Chen Y; Zhao M; Dong X
    Sci Total Environ; 2017 Feb; 580():1268-1275. PubMed ID: 28011018
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Classification and virtual screening of androgen receptor antagonists.
    Li J; Gramatica P
    J Chem Inf Model; 2010 May; 50(5):861-74. PubMed ID: 20405856
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.
    Marrero-Ponce Y; Iyarreta-Veitía M; Montero-Torres A; Romero-Zaldivar C; Brandt CA; Avila PE; Kirchgatter K; Machado Y
    J Chem Inf Model; 2005; 45(4):1082-100. PubMed ID: 16045304
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets.
    Ng HW; Doughty SW; Luo H; Ye H; Ge W; Tong W; Hong H
    Chem Res Toxicol; 2015 Dec; 28(12):2343-51. PubMed ID: 26524122
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets.
    Shen MY; Su BH; Esposito EX; Hopfinger AJ; Tseng YJ
    Chem Res Toxicol; 2011 Jun; 24(6):934-49. PubMed ID: 21504223
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays.
    Norinder U; Boyer S
    Chem Res Toxicol; 2016 Jun; 29(6):1003-10. PubMed ID: 27152554
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Antiprotozoan lead discovery by aligning dry and wet screening: prediction, synthesis, and biological assay of novel quinoxalinones.
    Martins Alho MA; Marrero-Ponce Y; Barigye SJ; Meneses-Marcel A; Machado Tugores Y; Montero-Torres A; Gómez-Barrio A; Nogal JJ; García-Sánchez RN; Vega MC; Rolón M; Martínez-Fernández AR; Escario JA; Pérez-Giménez F; Garcia-Domenech R; Rivera N; Mondragón R; Mondragón M; Ibarra-Velarde F; Lopez-Arencibia A; Martín-Navarro C; Lorenzo-Morales J; Cabrera-Serra MG; Piñero J; Tytgat J; Chicharro R; Arán VJ
    Bioorg Med Chem; 2014 Mar; 22(5):1568-85. PubMed ID: 24513185
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identification of active and inactive agonists/antagonists of estrogen receptor based on Tox21 10K compound library: Binomial analysis and structure alert.
    Wang J; Huang Y; Wang S; Yang Y; He J; Li C; Zhao YH; Martyniuk CJ
    Ecotoxicol Environ Saf; 2021 May; 214():112114. PubMed ID: 33711575
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals.
    Hornung MW; Tapper MA; Denny JS; Kolanczyk RC; Sheedy BR; Hartig PC; Aladjov H; Henry TR; Schmieder PK
    SAR QSAR Environ Res; 2014; 25(4):289-323. PubMed ID: 24779616
    [TBL] [Abstract][Full Text] [Related]  

  • 15. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles.
    Liu H; Papa E; Gramatica P
    Chem Res Toxicol; 2006 Nov; 19(11):1540-8. PubMed ID: 17112243
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Real-time growth kinetics measuring hormone mimicry for ToxCast chemicals in T-47D human ductal carcinoma cells.
    Rotroff DM; Dix DJ; Houck KA; Kavlock RJ; Knudsen TB; Martin MT; Reif DM; Richard AM; Sipes NS; Abassi YA; Jin C; Stampfl M; Judson RS
    Chem Res Toxicol; 2013 Jul; 26(7):1097-107. PubMed ID: 23682706
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Binary classification models for endocrine disrupter effects mediated through the estrogen receptor.
    Roncaglioni A; Piclin N; Pintore M; Benfenati E
    SAR QSAR Environ Res; 2008; 19(7-8):697-733. PubMed ID: 19061085
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.
    Russo DP; Zorn KM; Clark AM; Zhu H; Ekins S
    Mol Pharm; 2018 Oct; 15(10):4361-4370. PubMed ID: 30114914
    [TBL] [Abstract][Full Text] [Related]  

  • 19. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.
    Mansouri K; Abdelaziz A; Rybacka A; Roncaglioni A; Tropsha A; Varnek A; Zakharov A; Worth A; Richard AM; Grulke CM; Trisciuzzi D; Fourches D; Horvath D; Benfenati E; Muratov E; Wedebye EB; Grisoni F; Mangiatordi GF; Incisivo GM; Hong H; Ng HW; Tetko IV; Balabin I; Kancherla J; Shen J; Burton J; Nicklaus M; Cassotti M; Nikolov NG; Nicolotti O; Andersson PL; Zang Q; Politi R; Beger RD; Todeschini R; Huang R; Farag S; Rosenberg SA; Slavov S; Hu X; Judson RS
    Environ Health Perspect; 2016 Jul; 124(7):1023-33. PubMed ID: 26908244
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.
    Fang J; Yang R; Gao L; Zhou D; Yang S; Liu AL; Du GH
    J Chem Inf Model; 2013 Nov; 53(11):3009-20. PubMed ID: 24144102
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 53.