BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

247 related articles for article (PubMed ID: 20546594)

  • 21. Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect.
    Shi M; Umbach DM; Wise AS; Weinberg CR
    BMC Bioinformatics; 2018 Jan; 19(1):2. PubMed ID: 29291710
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Exploiting Linkage Disequilibrium for Ultrahigh-Dimensional Genome-Wide Data with an Integrated Statistical Approach.
    Carlsen M; Fu G; Bushman S; Corcoran C
    Genetics; 2016 Feb; 202(2):411-26. PubMed ID: 26661113
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Haplotype-based approach to known MS-associated regions increases the amount of explained risk.
    Khankhanian P; Gourraud PA; Lizee A; Goodin DS
    J Med Genet; 2015 Sep; 52(9):587-94. PubMed ID: 26185143
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies.
    Yaldız B; Erdoğan O; Rafatov S; Iyigün C; Aydın Son Y
    BioData Min; 2024 Jan; 17(1):3. PubMed ID: 38291454
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Scalable linkage-disequilibrium-based selective sweep detection: a performance guide.
    Alachiotis N; Pavlidis P
    Gigascience; 2016; 5():7. PubMed ID: 26862394
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm.
    Chuang LC; Kuo PH
    Sci Rep; 2017 Jan; 7():39943. PubMed ID: 28045094
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies.
    Hoggart CJ; Whittaker JC; De Iorio M; Balding DJ
    PLoS Genet; 2008 Jul; 4(7):e1000130. PubMed ID: 18654633
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Gene-gene interaction filtering with ensemble of filters.
    Yang P; Ho JW; Yang YH; Zhou BB
    BMC Bioinformatics; 2011 Feb; 12 Suppl 1(Suppl 1):S10. PubMed ID: 21342539
    [TBL] [Abstract][Full Text] [Related]  

  • 29. SNP interaction detection with Random Forests in high-dimensional genetic data.
    Winham SJ; Colby CL; Freimuth RR; Wang X; de Andrade M; Huebner M; Biernacka JM
    BMC Bioinformatics; 2012 Jul; 13():164. PubMed ID: 22793366
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Exploring the Genetic Patterns of Complex Diseases via the Integrative Genome-Wide Approach.
    Teng B; Yang C; Liu J; Cai Z; Wan X
    IEEE/ACM Trans Comput Biol Bioinform; 2016; 13(3):557-64. PubMed ID: 27295639
    [TBL] [Abstract][Full Text] [Related]  

  • 31. MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study.
    Wan X; Yang C; Yang Q; Xue H; Tang NL; Yu W
    BMC Bioinformatics; 2009 Jan; 10():13. PubMed ID: 19134182
    [TBL] [Abstract][Full Text] [Related]  

  • 32. TRM: a powerful two-stage machine learning approach for identifying SNP-SNP interactions.
    Lin HY; Chen YA; Tsai YY; Qu X; Tseng TS; Park JY
    Ann Hum Genet; 2012 Jan; 76(1):53-62. PubMed ID: 22150548
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Machine learning in genome-wide association studies.
    Szymczak S; Biernacka JM; Cordell HJ; González-Recio O; König IR; Zhang H; Sun YV
    Genet Epidemiol; 2009; 33 Suppl 1():S51-7. PubMed ID: 19924717
    [TBL] [Abstract][Full Text] [Related]  

  • 34. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data.
    Schwarz DF; König IR; Ziegler A
    Bioinformatics; 2010 Jul; 26(14):1752-8. PubMed ID: 20505004
    [TBL] [Abstract][Full Text] [Related]  

  • 35. 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]  

  • 36. Application of two machine learning algorithms to genetic association studies in the presence of covariates.
    Nonyane BA; Foulkes AS
    BMC Genet; 2008 Nov; 9():71. PubMed ID: 19014573
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Imputation and Reanalysis of ExomeChip Data Identifies Novel, Conditional and Joint Genetic Effects on Parkinson's Disease Risk.
    Rodrigo LM; Nyholt DR
    Genes (Basel); 2021 May; 12(5):. PubMed ID: 34064523
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.
    Taşan M; Musso G; Hao T; Vidal M; MacRae CA; Roth FP
    Nat Methods; 2015 Feb; 12(2):154-9. PubMed ID: 25532137
    [TBL] [Abstract][Full Text] [Related]  

  • 39. 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]  

  • 40. A fast algorithm for genome-wide haplotype pattern mining.
    Besenbacher S; Pedersen CN; Mailund T
    BMC Bioinformatics; 2009 Jan; 10 Suppl 1(Suppl 1):S74. PubMed ID: 19208179
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 13.