193 related articles for article (PubMed ID: 21718536)
1. Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiers.
Xu M; Tantisira KG; Wu A; Litonjua AA; Chu JH; Himes BE; Damask A; Weiss ST
BMC Med Genet; 2011 Jun; 12():90. PubMed ID: 21718536
[TBL] [Abstract][Full Text] [Related]
2. Development of a Pharmacogenetic Predictive Test in asthma: proof of concept.
Wu AC; Himes BE; Lasky-Su J; Litonjua A; Li L; Lange C; Lima J; Irvin CG; Weiss ST
Pharmacogenet Genomics; 2010 Feb; 20(2):86-93. PubMed ID: 20032818
[TBL] [Abstract][Full Text] [Related]
3. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.
Nguyen TT; Huang J; Wu Q; Nguyen T; Li M
BMC Genomics; 2015; 16 Suppl 2(Suppl 2):S5. PubMed ID: 25708662
[TBL] [Abstract][Full Text] [Related]
4. Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors.
Krautenbacher N; Kabesch M; Horak E; Braun-Fahrländer C; Genuneit J; Boznanski A; von Mutius E; Theis F; Fuchs C; Ege MJ;
Pediatr Allergy Immunol; 2021 Feb; 32(2):295-304. PubMed ID: 32997854
[TBL] [Abstract][Full Text] [Related]
5. Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.
Wang Y; Goh W; Wong L; Montana G;
BMC Bioinformatics; 2013; 14 Suppl 16(Suppl 16):S6. PubMed ID: 24564704
[TBL] [Abstract][Full Text] [Related]
6. CTNNA3 and SEMA3D: Promising loci for asthma exacerbation identified through multiple genome-wide association studies.
McGeachie MJ; Wu AC; Tse SM; Clemmer GL; Sordillo J; Himes BE; Lasky-Su J; Chase RP; Martinez FD; Weeke P; Shaffer CM; Xu H; Denny JC; Roden DM; Panettieri RA; Raby BA; Weiss ST; Tantisira KG
J Allergy Clin Immunol; 2015 Dec; 136(6):1503-1510. PubMed ID: 26073756
[TBL] [Abstract][Full Text] [Related]
7. Whole genome prediction and heritability of childhood asthma phenotypes.
McGeachie MJ; Clemmer GL; Croteau-Chonka DC; Castaldi PJ; Cho MH; Sordillo JE; Lasky-Su JA; Raby BA; Tantisira KG; Weiss ST
Immun Inflamm Dis; 2016 Dec; 4(4):487-496. PubMed ID: 27980782
[TBL] [Abstract][Full Text] [Related]
8. Novel Machine Learning Can Predict Acute Asthma Exacerbation.
Zein JG; Wu CP; Attaway AH; Zhang P; Nazha A
Chest; 2021 May; 159(5):1747-1757. PubMed ID: 33440184
[TBL] [Abstract][Full Text] [Related]
9. Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches.
Qin ZM; Liang SQ; Long JX; Deng JM; Wei X; Yang ML; Tang SJ; Li HL
Comb Chem High Throughput Screen; 2024; 27(3):400-407. PubMed ID: 37278039
[TBL] [Abstract][Full Text] [Related]
10. Single Nucleotide Polymorphisms (SNPs) in PRKG1 & SPATA13-AS1 are associated with bronchodilator response: a pilot study during acute asthma exacerbations in African American children.
Fishe JN; Labilloy G; Higley R; Casey D; Ginn A; Baskovich B; Blake KV
Pharmacogenet Genomics; 2021 Sep; 31(7):146-154. PubMed ID: 33851947
[TBL] [Abstract][Full Text] [Related]
11. Genome-wide association analysis of circulating vitamin D levels in children with asthma.
Lasky-Su J; Lange N; Brehm JM; Damask A; Soto-Quiros M; Avila L; Celedón JC; Canino G; Cloutier MM; Hollis BW; Weiss ST; Litonjua AA
Hum Genet; 2012 Sep; 131(9):1495-505. PubMed ID: 22673963
[TBL] [Abstract][Full Text] [Related]
12. Machine learning approach to single nucleotide polymorphism-based asthma prediction.
Gaudillo J; Rodriguez JJR; Nazareno A; Baltazar LR; Vilela J; Bulalacao R; Domingo M; Albia J
PLoS One; 2019; 14(12):e0225574. PubMed ID: 31800601
[TBL] [Abstract][Full Text] [Related]
13. Predicting Severe Asthma Exacerbations in Children: Blueprint for Today and Tomorrow.
Navanandan N; Hatoun J; Celedón JC; Liu AH
J Allergy Clin Immunol Pract; 2021 Jul; 9(7):2619-2626. PubMed ID: 33831622
[TBL] [Abstract][Full Text] [Related]
14. Genome-wide association identifies the T gene as a novel asthma pharmacogenetic locus.
Tantisira KG; Damask A; Szefler SJ; Schuemann B; Markezich A; Su J; Klanderman B; Sylvia J; Wu R; Martinez F; Boushey HA; Chinchilli VM; Mauger D; Weiss ST; Israel E;
Am J Respir Crit Care Med; 2012 Jun; 185(12):1286-91. PubMed ID: 22538805
[TBL] [Abstract][Full Text] [Related]
15. Polymorphisms in IL13, total IgE, eosinophilia, and asthma exacerbations in childhood.
Hunninghake GM; Soto-Quirós ME; Avila L; Su J; Murphy A; Demeo DL; Ly NP; Liang C; Sylvia JS; Klanderman BJ; Lange C; Raby BA; Silverman EK; Celedón JC
J Allergy Clin Immunol; 2007 Jul; 120(1):84-90. PubMed ID: 17561245
[TBL] [Abstract][Full Text] [Related]
16. Identifying SNPs predictive of phenotype using random forests.
Bureau A; Dupuis J; Falls K; Lunetta KL; Hayward B; Keith TP; Van Eerdewegh P
Genet Epidemiol; 2005 Feb; 28(2):171-82. PubMed ID: 15593090
[TBL] [Abstract][Full Text] [Related]
17. CMTR1 is associated with increased asthma exacerbations in patients taking inhaled corticosteroids.
Dahlin A; Denny J; Roden DM; Brilliant MH; Ingram C; Kitchner TE; Linneman JG; Shaffer CM; Weeke P; Xu H; Kubo M; Tamari M; Clemmer GL; Ziniti J; McGeachie MJ; Tantisira KG; Weiss ST; Wu AC
Immun Inflamm Dis; 2015 Dec; 3(4):350-9. PubMed ID: 26734457
[TBL] [Abstract][Full Text] [Related]
18. Random Forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle.
Yao C; Spurlock DM; Armentano LE; Page CD; VandeHaar MJ; Bickhart DM; Weigel KA
J Dairy Sci; 2013 Oct; 96(10):6716-29. PubMed ID: 23932129
[TBL] [Abstract][Full Text] [Related]
19. Genetic determinants of acute asthma therapy response in children with moderate-to-severe asthma exacerbations.
Tse SM; Krajinovic M; Chauhan BF; Zemek R; Gravel J; Chalut D; Poonai N; Quach C; Laberge S; Ducharme FM;
Pediatr Pulmonol; 2019 Apr; 54(4):378-385. PubMed ID: 30644648
[TBL] [Abstract][Full Text] [Related]
20. Multigenic modeling of complex disease by random forests.
Sun YV
Adv Genet; 2010; 72():73-99. PubMed ID: 21029849
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]