143 related articles for article (PubMed ID: 25592581)
1. Variable selection method for the identification of epistatic models.
Holzinger ER; Szymczak S; Dasgupta A; Malley J; Li Q; Bailey-Wilson JE
Pac Symp Biocomput; 2015; 20():195-206. PubMed ID: 25592581
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. r2VIM: A new variable selection method for random forests in genome-wide association studies.
Szymczak S; Holzinger E; Dasgupta A; Malley JD; Molloy AM; Mills JL; Brody LC; Stambolian D; Bailey-Wilson JE
BioData Min; 2016; 9():7. PubMed ID: 26839594
[TBL] [Abstract][Full Text] [Related]
4. Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks.
Han B; Chen XW; Talebizadeh Z; Xu H
BMC Syst Biol; 2012; 6 Suppl 3(Suppl 3):S14. PubMed ID: 23281790
[TBL] [Abstract][Full Text] [Related]
5. Comparison of parametric and machine methods for variable selection in simulated Genetic Analysis Workshop 19 data.
Holzinger ER; Szymczak S; Malley J; Pugh EW; Ling H; Griffith S; Zhang P; Li Q; Cropp CD; Bailey-Wilson JE
BMC Proc; 2016; 10(Suppl 7):147-152. PubMed ID: 27980627
[TBL] [Abstract][Full Text] [Related]
6. Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction.
García-Magariños M; López-de-Ullibarri I; Cao R; Salas A
Ann Hum Genet; 2009 May; 73(Pt 3):360-9. PubMed ID: 19291098
[TBL] [Abstract][Full Text] [Related]
7. A Markov blanket-based method for detecting causal SNPs in GWAS.
Han B; Park M; Chen XW
BMC Bioinformatics; 2010 Apr; 11 Suppl 3(Suppl 3):S5. PubMed ID: 20438652
[TBL] [Abstract][Full Text] [Related]
8. Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women.
Fergus P; Montanez CC; Abdulaimma B; Lisboa P; Chalmers C; Pineles B
IEEE/ACM Trans Comput Biol Bioinform; 2020; 17(2):668-678. PubMed ID: 30183645
[TBL] [Abstract][Full Text] [Related]
9. Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits.
Crawford L; Zeng P; Mukherjee S; Zhou X
PLoS Genet; 2017 Jul; 13(7):e1006869. PubMed ID: 28746338
[TBL] [Abstract][Full Text] [Related]
10. Detecting genetic interactions in pathway-based genome-wide association studies.
Huang A; Martin ER; Vance JM; Cai X
Genet Epidemiol; 2014 May; 38(4):300-9. PubMed ID: 24719383
[TBL] [Abstract][Full Text] [Related]
11. bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies.
Han B; Chen XW
BMC Genomics; 2011; 12 Suppl 2(Suppl 2):S9. PubMed ID: 21989368
[TBL] [Abstract][Full Text] [Related]
12. High-throughput analysis of epistasis in genome-wide association studies with BiForce.
Gyenesei A; Moody J; Semple CA; Haley CS; Wei WH
Bioinformatics; 2012 Aug; 28(15):1957-64. PubMed ID: 22618535
[TBL] [Abstract][Full Text] [Related]
13. A whole-genome simulator capable of modeling high-order epistasis for complex disease.
Yang W; Gu CC
Genet Epidemiol; 2013 Nov; 37(7):686-94. PubMed ID: 24114848
[TBL] [Abstract][Full Text] [Related]
14. AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects.
Zhang Q; Long Q; Ott J
PLoS Comput Biol; 2014 Jun; 10(6):e1003627. PubMed ID: 24901472
[TBL] [Abstract][Full Text] [Related]
15. How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?
Veturi Y; Ritchie MD
Pac Symp Biocomput; 2018; 23():228-239. PubMed ID: 29218884
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies.
Wang J; Joshi T; Valliyodan B; Shi H; Liang Y; Nguyen HT; Zhang J; Xu D
BMC Genomics; 2015 Nov; 16():1011. PubMed ID: 26607428
[TBL] [Abstract][Full Text] [Related]
18. A deep hybrid model to detect multi-locus interacting SNPs in the presence of noise.
Uppu S; Krishna A
Int J Med Inform; 2018 Nov; 119():134-151. PubMed ID: 30342681
[TBL] [Abstract][Full Text] [Related]
19. WISH-R- a fast and efficient tool for construction of epistatic networks for complex traits and diseases.
Carmelo VAO; Kogelman LJA; Madsen MB; Kadarmideen HN
BMC Bioinformatics; 2018 Jul; 19(1):277. PubMed ID: 30064383
[TBL] [Abstract][Full Text] [Related]
20. Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies.
Li X; Zhang S; Wong KC
IEEE/ACM Trans Comput Biol Bioinform; 2020; 17(1):226-237. PubMed ID: 29994485
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]