BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

161 related articles for article (PubMed ID: 22150548)

  • 1. 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]  

  • 2. Comparison of multivariate adaptive regression splines and logistic regression in detecting SNP-SNP interactions and their application in prostate cancer.
    Lin HY; Wang W; Liu YH; Soong SJ; York TP; Myers L; Hu JJ
    J Hum Genet; 2008; 53(9):802-811. PubMed ID: 18607530
    [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. 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]  

  • 5. 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]  

  • 6. Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed.
    Anmala J; Turuganti V
    Water Environ Res; 2021 Nov; 93(11):2360-2373. PubMed ID: 34528328
    [TBL] [Abstract][Full Text] [Related]  

  • 7. SNP-SNP interaction network in angiogenesis genes associated with prostate cancer aggressiveness.
    Lin HY; Amankwah EK; Tseng TS; Qu X; Chen DT; Park JY
    PLoS One; 2013; 8(4):e59688. PubMed ID: 23593148
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Screening Discriminating SNPs for Chinese Indigenous Pig Breeds Identification Using a Random Forests Algorithm.
    Gao J; Sun L; Zhang S; Xu J; He M; Zhang D; Wu C; Dai J
    Genes (Basel); 2022 Nov; 13(12):. PubMed ID: 36553474
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Bootstrap aggregating of alternating decision trees to detect sets of SNPs that associate with disease.
    Guy RT; Santago P; Langefeld CD
    Genet Epidemiol; 2012 Feb; 36(2):99-106. PubMed ID: 22851473
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias.
    Winham SJ; Jenkins GD; Biernacka JM
    Genet Epidemiol; 2016 Feb; 40(2):123-32. PubMed ID: 26639183
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction.
    López B; Torrent-Fontbona F; Viñas R; Fernández-Real JM
    Artif Intell Med; 2018 Apr; 85():43-49. PubMed ID: 28943335
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds.
    Schiavo G; Bertolini F; Galimberti G; Bovo S; Dall'Olio S; Nanni Costa L; Gallo M; Fontanesi L
    Animal; 2020 Feb; 14(2):223-232. PubMed ID: 31603060
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury.
    Moore R; Ashby K; Liao TJ; Chen M
    Int J Environ Res Public Health; 2021 Oct; 18(20):. PubMed ID: 34682349
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. SNP selection and classification of genome-wide SNP data using stratified sampling random forests.
    Wu Q; Ye Y; Liu Y; Ng MK
    IEEE Trans Nanobioscience; 2012 Sep; 11(3):216-27. PubMed ID: 22987127
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning Methods.
    Li B; Zhang N; Wang YG; George AW; Reverter A; Li Y
    Front Genet; 2018; 9():237. PubMed ID: 30023001
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine Learning as an Effective Method for Identifying True Single Nucleotide Polymorphisms in Polyploid Plants.
    Korani W; Clevenger JP; Chu Y; Ozias-Akins P
    Plant Genome; 2019 Mar; 12(1):. PubMed ID: 30951095
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Supervised learning-based tagSNP selection for genome-wide disease classifications.
    Liu Q; Yang J; Chen Z; Yang MQ; Sung AH; Huang X
    BMC Genomics; 2008; 9 Suppl 1(Suppl 1):S6. PubMed ID: 18366619
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.