BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

102 related articles for article (PubMed ID: 30158504)

  • 1. FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies.
    Tuo S
    Genes (Basel); 2018 Aug; 9(9):. PubMed ID: 30158504
    [TBL] [Abstract][Full Text] [Related]  

  • 2. HiSSI: high-order SNP-SNP interactions detection based on efficient significant pattern and differential evolution.
    Cao X; Liu J; Guo M; Wang J
    BMC Med Genomics; 2019 Dec; 12(Suppl 7):139. PubMed ID: 31888641
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions.
    Tuo S; Li C; Liu F; Zhu Y; Chen T; Feng Z; Liu H; Li A
    Interdiscip Sci; 2022 Dec; 14(4):814-832. PubMed ID: 35788965
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.
    Tuo S; Zhang J; Yuan X; He Z; Liu Y; Liu Z
    Sci Rep; 2017 Sep; 7(1):11529. PubMed ID: 28912584
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism.
    Woo HJ; Yu C; Kumar K; Gold B; Reifman J
    BMC Genomics; 2016 Aug; 17(1):695. PubMed ID: 27576376
    [TBL] [Abstract][Full Text] [Related]  

  • 6. ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information.
    Cao X; Yu G; Liu J; Jia L; Wang J
    Int J Mol Sci; 2018 Aug; 19(8):. PubMed ID: 30072632
    [TBL] [Abstract][Full Text] [Related]  

  • 7. EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm.
    Chen Y; Xu F; Pian C; Xu M; Kong L; Fang J; Li Z; Zhang L
    Genes (Basel); 2021 Jan; 12(2):. PubMed ID: 33525573
    [TBL] [Abstract][Full Text] [Related]  

  • 8. HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations.
    Liu J; Yu G; Jiang Y; Wang J
    Genes (Basel); 2017 May; 8(6):. PubMed ID: 28561745
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A random forest approach to the detection of epistatic interactions in case-control studies.
    Jiang R; Tang W; Wu X; Fu W
    BMC Bioinformatics; 2009 Jan; 10 Suppl 1(Suppl 1):S65. PubMed ID: 19208169
    [TBL] [Abstract][Full Text] [Related]  

  • 10. EpiMC: Detecting Epistatic Interactions Using Multiple Clusterings.
    Wang J; Zhang H; Ren W; Guo M; Yu G
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(1):243-254. PubMed ID: 33989157
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure.
    Leem S; Jeong HH; Lee J; Wee K; Sohn KA
    Comput Biol Chem; 2014 Jun; 50():19-28. PubMed ID: 24581733
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations.
    Lee S; Lozano A; Kambadur P; Xing EP
    J Comput Biol; 2016 May; 23(5):372-89. PubMed ID: 27159633
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Evaluation of Existing Methods for High-Order Epistasis Detection.
    Ponte-Fernandez C; Gonzalez-Dominguez J; Carvajal-Rodriguez A; Martin MJ
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(2):912-926. PubMed ID: 33055017
    [TBL] [Abstract][Full Text] [Related]  

  • 14. KDSNP: A kernel-based approach to detecting high-order SNP interactions.
    Kodama K; Saigo H
    J Bioinform Comput Biol; 2016 Oct; 14(5):1644003. PubMed ID: 27806683
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A novel two-stage approach for epistasis detection in genome-wide case-control studies.
    Liao Z; Zeng Q; Liao B; Li X
    Biochem Genet; 2014 Oct; 52(9-10):403-14. PubMed ID: 24880910
    [TBL] [Abstract][Full Text] [Related]  

  • 16. EpiHNet: Detecting epistasis by heterogeneous molecule network.
    Wang X; Zhang H; Wang J; Yu G; Cui L; Guo M
    Methods; 2022 Feb; 198():65-75. PubMed ID: 34555529
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Multipopulation harmony search algorithm for the detection of high-order SNP interactions.
    Tuo S; Liu H; Chen H
    Bioinformatics; 2020 Aug; 36(16):4389-4398. PubMed ID: 32227192
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies.
    Yang C; He Z; Wan X; Yang Q; Xue H; Yu W
    Bioinformatics; 2009 Feb; 25(4):504-11. PubMed ID: 19098029
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection.
    Gu Y; Sun Y; Shang J; Li F; Guan B; Liu JX
    Genes (Basel); 2022 May; 13(5):. PubMed ID: 35627256
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.