[1] |
Metzker ML. Sequencing technologies - the next generation [J]. Nat Rev Genet, 2010, 11(1): 31-46.
|
[2] |
Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities [J]. Nat Rev Genet, 2011, 12(2): 87-98.
|
[3] |
Laird PW. Principles and challenges of genomewide DNA methylation analysis [J]. Nat Rev Genet, 2010, 11(3): 191-203.
|
[4] |
Altelaar AF, Munoz J, Heck AJ. Next-generation proteomics: towards an integrative view of proteome dynamics [J]. Nat Rev Genet, 2013, 14(1): 35-48.
|
[5] |
Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma [J]. Nature, 2011, 474(7353): 609-615.
|
[6] |
Cancer Genome Atlas Research Network, Kandoth C, Schultz N, et al. Integrated genomic characterization of endometrial carcinoma [J]. Nature, 2013, 497(7447): 67-73.
|
[7] |
Zhang H, Liu T, Zhang Z, et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer [J]. Cell, 2016, 166(3): 755-765.
|
[8] |
Rubingh CM, Bijlsma S, Derks EP, et al. Assessing the performance of statistical validation tools for megavariate metabolomics data [J]. Metabolomics, 2006, 2(2): 53-61.
|
[9] |
Brougham DF, Ivanova G, Gottschalk M, et al. Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance [J]. J Biomed Biotechnol, 2011, 2011: 158094.
|
[10] |
Ritchie MD, Holzinger ER, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions [J]. Nat Rev Genet, 2015, 16(2): 85-97.
|
[11] |
Fridley BL, Lund S, Jenkins GD, et al. A Bayesian integrative genomic model for pathway analysis of complex traits [J]. Genet Epidemiol, 2012, 36(4): 352-359.
|
[12] |
Lanckriet GR, De Bie T, Cristianini N, et al. A statistical framework for genomic data fusion [J]. Bioinformatics, 2004, 20(16): 2626-2635.
|
[13] |
Kim D, Shin H, Song YS, et al. Synergistic effect of different levels of genomic data for cancer clinical outcome prediction [J]. J Biomed Inform, 2012, 45(6): 1191-1198.
|
[14] |
Kanehisa M, Furumichi M, Tanabe M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs [J]. Nucleic Acids Res, 2017, 45(D1): D353-D361.
|
[15] |
Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life [J]. Nucleic Acids Res, 2015, 43(Database issue): D447-D452.
|
[16] |
Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants [J]. Nucleic Acids Res, 2012, 40(Database issue): D930-D934.
|
[17] |
Boyle AP, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB [J]. Genome Res, 2012, 22(9): 1790-1797.
|
[18] |
Amberger JS, Bocchini CA, Schiettecatte F, et al. OMIM.org: Online Mendelian Inheritance in Man (OMIM(R), an online catalog of human genes and genetic disorders [J]. Nucleic Acids Res, 2015, 43(Database issue): D789-D798.
|
[19] |
Forbes SA, Bindal N, Bamford S, et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer [J]. Nucleic Acids Res, 2011, 39(Database issue): D945-D950.
|
[20] |
Welter D, MacArthur J, Morales J, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations [J]. Nucleic Acids Res, 2014, 42(Database issue): D1001-D1006.
|
[21] |
Feng C, Xiong Z, Jiang H, et al. Genetic alteration in notch pathway is associated with better prognosis in renal cell carcinoma [J]. Biofactors, 2016, 42(1): 41-48.
|
[22] |
Kottgen A, Glazer NL, Dehghan A, et al. Multiple loci associated with indices of renal function and chronic kidney disease [J]. Nat Genet, 2009, 41(6): 712-717.
|
[23] |
Kottgen A, Hwang SJ, Larson MG, et al. Uromodulin levels associate with a common UMOD variant and risk for incident CKD [J]. J Am Soc Nephrol, 2010, 21(2): 337-344.
|
[24] |
Pattaro C, Kottgen A, Teumer A, et al. Genome-wide association and functional follow-up reveals new loci for kidney function [J]. PLoS Genet, 2012, 8(3): e1002584.
|
[25] |
Sandholm N, Salem RM, McKnight AJ, et al. New susceptibility loci associated with kidney disease in type 1 diabetes [J]. PLoS Genet, 2012, 8(9): e1002921.
|
[26] |
Yu XQ, Li M, Zhang H, et al. A genome-wide association study in Han Chinese identifies multiple susceptibility loci for IgA nephropathy [J]. Nat Genet, 2011, 44(2): 178-182.
|
[27] |
Otto EA, Hurd TW, Airik R, et al. Candidate exome capture identifies mutation of SDCCAG8 as the cause of a retinal-renal ciliopathy [J]. Nat Genet, 2010, 42(10): 840-850.
|
[28] |
Badal SS, Danesh FR. MicroRNAs and their applications in kidney diseases [J]. Pediatr Nephrol, 2015, 30(5): 727-740.
|
[29] |
Woroniecka KI, Park AS, Mohtat D, et al. Transcriptome analysis of human diabetic kidney disease [J]. Diabetes, 2011, 60(9): 2354-2369.
|
[30] |
Godwin JG, Ge X, Stephan K, et al. Identification of a microRNA signature of renal ischemia reperfusion injury [J]. Proc Natl Acad Sci USA, 2010, 107(32): 14339-14344.
|
[31] |
Schanstra JP, Mischak H. Proteomic urinary biomarker approach in renal disease: from discovery to implementation [J]. Pediatr Nephrol, 2015, 30(5): 713-725.
|
[32] |
Menon V, Shlipak MG, Wang X, et al. Cystatin C as a risk factor for outcomes in chronic kidney disease [J]. Ann Intern Med, 2007, 147(1): 19-27.
|
[33] |
Nauta FL, Boertien WE, Bakker SJ, et al. Glomerular and tubular damage markers are elevated in patients with diabetes [J]. Diabetes Care, 2011, 34(4): 975-981.
|
[34] |
Mitsnefes MM, Kathman TS, Mishra J, et al. Serum neutrophil gelatinase-associated lipocalin as a marker of renal function in children with chronic kidney disease [J]. Pediatr Nephrol, 2007, 22(1): 101-108.
|
[35] |
Hirayama A, Nakashima E, Sugimoto M, et al. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy [J]. Anal Bioanal Chem, 2012, 404(10): 3101-3109.
|
[36] |
Toyohara T, Akiyama Y, Suzuki T, et al. Metabolomic profiling of uremic solutes in CKD patients [J]. Hypertens Res, 2010, 33(9): 944-952.
|
[37] |
Smyth LJ, McKay GJ, Maxwell AP, et al. DNA hypermethylation and DNA hypomethylation is present at different loci in chronic kidney disease [J]. Epigenetics, 2014, 9(3): 366-376.
|
[38] |
Ko YA, Mohtat D, Suzuki M, et al. Cytosine methylation changes in enhancer regions of core pro-fibrotic genes characterize kidney fibrosis development [J]. Genome Biol, 2013, 14(10): R108.
|
[39] |
Pesce F, Pathan S, Schena FP. From-omics to personalized medicine in nephrology: integration is the key [J]. Nephrol Dial Transplant, 2013, 28(1): 24-28.
|