230 related articles for article (PubMed ID: 28575147)
1. Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules.
Yao X; Yan J; Liu K; Kim S; Nho K; Risacher SL; Greene CS; Moore JH; Saykin AJ; Shen L;
Bioinformatics; 2017 Oct; 33(20):3250-3257. PubMed ID: 28575147
[TBL] [Abstract][Full Text] [Related]
2. NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES.
Yao X; Yan J; Risacher S; Moore J; Saykin A; Shen L
Proc IEEE Int Conf Acoust Speech Signal Process; 2017; 2017():6170-6174. PubMed ID: 28989328
[TBL] [Abstract][Full Text] [Related]
3. SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network.
Liu Y; Brossard M; Roqueiro D; Margaritte-Jeannin P; Sarnowski C; Bouzigon E; Demenais F
Bioinformatics; 2017 May; 33(10):1536-1544. PubMed ID: 28069594
[TBL] [Abstract][Full Text] [Related]
4. Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease.
Li J; Chen F; Zhang Q; Meng X; Yao X; Risacher SL; Yan J; Saykin AJ; Liang H; Shen L;
Curr Alzheimer Res; 2019; 16(13):1163-1174. PubMed ID: 31755389
[TBL] [Abstract][Full Text] [Related]
5. Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer's disease.
Kim M; Wu R; Yao X; Saykin AJ; Moore JH; Shen L;
BMC Med Genomics; 2022 Aug; 15(Suppl 2):168. PubMed ID: 35915443
[TBL] [Abstract][Full Text] [Related]
6. Identification of susceptibility modules for coronary artery disease using a genome wide integrated network analysis.
Duan S; Luo X; Dong C
Gene; 2013 Dec; 531(2):347-54. PubMed ID: 23994195
[TBL] [Abstract][Full Text] [Related]
7. Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease.
Meng X; Li J; Zhang Q; Chen F; Bian C; Yao X; Yan J; Xu Z; Risacher SL; Saykin AJ; Liang H; Shen L;
BMC Genomics; 2020 Dec; 21(Suppl 11):896. PubMed ID: 33372590
[TBL] [Abstract][Full Text] [Related]
8. Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.
Hao X; Li C; Yan J; Yao X; Risacher SL; Saykin AJ; Shen L; Zhang D;
Bioinformatics; 2017 Jul; 33(14):i341-i349. PubMed ID: 28881979
[TBL] [Abstract][Full Text] [Related]
9. Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort.
Cong W; Meng X; Li J; Zhang Q; Chen F; Liu W; Wang Y; Cheng S; Yao X; Yan J; Kim S; Saykin AJ; Liang H; Shen L;
BMC Genomics; 2017 May; 18(1):421. PubMed ID: 28558704
[TBL] [Abstract][Full Text] [Related]
10. A novel approach for multi-SNP GWAS and its application in Alzheimer's disease.
Bodily PM; Fujimoto MS; Page JT; Clement MJ; Ebbert MT; Ridge PG;
BMC Bioinformatics; 2016 Jul; 17 Suppl 7(Suppl 7):268. PubMed ID: 27453991
[TBL] [Abstract][Full Text] [Related]
11. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks.
Jia P; Zheng S; Long J; Zheng W; Zhao Z
Bioinformatics; 2011 Jan; 27(1):95-102. PubMed ID: 21045073
[TBL] [Abstract][Full Text] [Related]
12. A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease.
Squillario M; Abate G; Tomasi F; Tozzo V; Barla A; Uberti D;
Sci Rep; 2020 Jul; 10(1):12063. PubMed ID: 32694537
[TBL] [Abstract][Full Text] [Related]
13. Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning.
Wu M; Zeng W; Liu W; Lv H; Chen T; Jiang R
Methods; 2018 Aug; 145():41-50. PubMed ID: 29874547
[TBL] [Abstract][Full Text] [Related]
14. Bioinformatics strategy to advance the interpretation of Alzheimer's disease GWAS discoveries: The roads from association to causation.
Lutz MW; Sprague D; Chiba-Falek O
Alzheimers Dement; 2019 Aug; 15(8):1048-1058. PubMed ID: 31262699
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Regional imaging genetic enrichment analysis.
Yao X; Cong S; Yan J; Risacher SL; Saykin AJ; Moore JH; Shen L; ;
Bioinformatics; 2020 Apr; 36(8):2554-2560. PubMed ID: 31860065
[TBL] [Abstract][Full Text] [Related]
17. GWAS Integrator: a bioinformatics tool to explore human genetic associations reported in published genome-wide association studies.
Yu W; Yesupriya A; Wulf A; Hindorff LA; Dowling N; Khoury MJ; Gwinn M
Eur J Hum Genet; 2011 Oct; 19(10):1095-9. PubMed ID: 21610748
[TBL] [Abstract][Full Text] [Related]
18. A network-based approach to prioritize results from genome-wide association studies.
Akula N; Baranova A; Seto D; Solka J; Nalls MA; Singleton A; Ferrucci L; Tanaka T; Bandinelli S; Cho YS; Kim YJ; Lee JY; Han BG; ; ; McMahon FJ
PLoS One; 2011; 6(9):e24220. PubMed ID: 21915301
[TBL] [Abstract][Full Text] [Related]
19. NetCore: a network propagation approach using node coreness.
Barel G; Herwig R
Nucleic Acids Res; 2020 Sep; 48(17):e98. PubMed ID: 32735660
[TBL] [Abstract][Full Text] [Related]
20. Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.
Zhou T; Thung KH; Liu M; Shen D
IEEE Trans Biomed Eng; 2019 Jan; 66(1):165-175. PubMed ID: 29993426
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
