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Items: 1 to 20 of 98

1.

Hippocampal transcriptome profiling combined with protein-protein interaction analysis elucidates Alzheimer's disease pathways and genes.

van Rooij JGJ, Meeter LHH, Melhem S, Nijholt DAT, Wong TH; Netherlands Brain Bank, Rozemuller A, Uitterlinden AG, van Meurs JG, van Swieten JC.

Neurobiol Aging. 2019 Feb;74:225-233. doi: 10.1016/j.neurobiolaging.2018.10.023. Epub 2018 Oct 29.

2.

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

Hu YS, Xin J, Hu Y, Zhang L, Wang J.

Alzheimers Res Ther. 2017 Apr 27;9(1):29. doi: 10.1186/s13195-017-0252-z.

3.

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer's disease patients.

Xiang S, Huang Z, Wang T, Han Z, Yu CY, Ni D, Huang K, Zhang J.

BMC Med Genomics. 2018 Dec 31;11(Suppl 6):115. doi: 10.1186/s12920-018-0431-1.

4.

Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer's disease.

Wang M, Roussos P, McKenzie A, Zhou X, Kajiwara Y, Brennand KJ, De Luca GC, Crary JF, Casaccia P, Buxbaum JD, Ehrlich M, Gandy S, Goate A, Katsel P, Schadt E, Haroutunian V, Zhang B.

Genome Med. 2016 Nov 1;8(1):104.

5.

Meta-analysis on blood transcriptomic studies identifies consistently coexpressed protein-protein interaction modules as robust markers of human aging.

van den Akker EB, Passtoors WM, Jansen R, van Zwet EW, Goeman JJ, Hulsman M, Emilsson V, Perola M, Willemsen G, Penninx BW, Heijmans BT, Maier AB, Boomsma DI, Kok JN, Slagboom PE, Reinders MJ, Beekman M.

Aging Cell. 2014 Apr;13(2):216-25. doi: 10.1111/acel.12160. Epub 2013 Nov 19.

6.

Identification of therapeutic targets for Alzheimer's disease via differentially expressed gene and weighted gene co-expression network analyses.

Jia Y, Nie K, Li J, Liang X, Zhang X.

Mol Med Rep. 2016 Nov;14(5):4844-4848. doi: 10.3892/mmr.2016.5828. Epub 2016 Oct 12.

PMID:
27748870
7.

Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer's disease.

Semick SA, Bharadwaj RA, Collado-Torres L, Tao R, Shin JH, Deep-Soboslay A, Weiss JR, Weinberger DR, Hyde TM, Kleinman JE, Jaffe AE, Mattay VS.

Acta Neuropathol. 2019 Apr;137(4):557-569. doi: 10.1007/s00401-019-01966-5. Epub 2019 Feb 2.

PMID:
30712078
8.

Analysis of transcription factor- and ncRNA-mediated potential pathogenic gene modules in Alzheimer's disease.

Zou C, Wang J, Huang X, Jian C, Zou D, Li X.

Aging (Albany NY). 2019 Aug 16;11(16):6109-6119. doi: 10.18632/aging.102169. Epub 2019 Aug 16.

9.

Intrinsic-overlapping co-expression module detection with application to Alzheimer's Disease.

Manners HN, Roy S, Kalita JK.

Comput Biol Chem. 2018 Dec;77:373-389. doi: 10.1016/j.compbiolchem.2018.10.014. Epub 2018 Nov 9.

PMID:
30466046
10.

Integrative Analysis of Hippocampus Gene Expression Profiles Identifies Network Alterations in Aging and Alzheimer's Disease.

Lanke V, Moolamalla STR, Roy D, Vinod PK.

Front Aging Neurosci. 2018 May 23;10:153. doi: 10.3389/fnagi.2018.00153. eCollection 2018.

11.

In silico analyses for molecular genetic mechanism and candidate genes in patients with Alzheimer's disease.

Chi LM, Wang X, Nan GX.

Acta Neurol Belg. 2016 Dec;116(4):543-547. Epub 2016 Mar 2.

PMID:
26935318
12.

Identification of unstable network modules reveals disease modules associated with the progression of Alzheimer's disease.

Kikuchi M, Ogishima S, Miyamoto T, Miyashita A, Kuwano R, Nakaya J, Tanaka H.

PLoS One. 2013 Nov 15;8(11):e76162. doi: 10.1371/journal.pone.0076162. eCollection 2013.

13.
14.

Potential hippocampal genes and pathways involved in Alzheimer's disease: a bioinformatic analysis.

Zhang L, Guo XQ, Chu JF, Zhang X, Yan ZR, Li YZ.

Genet Mol Res. 2015 Jun 29;14(2):7218-32. doi: 10.4238/2015.June.29.15.

15.

Gene Expression Profiling in the APP/PS1KI Mouse Model of Familial Alzheimer's Disease.

Weissmann R, Hüttenrauch M, Kacprowski T, Bouter Y, Pradier L, Bayer TA, Kuss AW, Wirths O.

J Alzheimers Dis. 2016;50(2):397-409. doi: 10.3233/JAD-150745.

PMID:
26639971
16.

Deep proteomic network analysis of Alzheimer's disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease.

Johnson ECB, Dammer EB, Duong DM, Yin L, Thambisetty M, Troncoso JC, Lah JJ, Levey AI, Seyfried NT.

Mol Neurodegener. 2018 Oct 4;13(1):52. doi: 10.1186/s13024-018-0282-4.

17.

Identification of Differentially Expressed Genes through Integrated Study of Alzheimer's Disease Affected Brain Regions.

Puthiyedth N, Riveros C, Berretta R, Moscato P.

PLoS One. 2016 Apr 6;11(4):e0152342. doi: 10.1371/journal.pone.0152342. eCollection 2016.

18.
19.

Integrated biology approach reveals molecular and pathological interactions among Alzheimer's Aβ42, Tau, TREM2, and TYROBP in Drosophila models.

Sekiya M, Wang M, Fujisaki N, Sakakibara Y, Quan X, Ehrlich ME, De Jager PL, Bennett DA, Schadt EE, Gandy S, Ando K, Zhang B, Iijima KM.

Genome Med. 2018 Mar 29;10(1):26. doi: 10.1186/s13073-018-0530-9.

20.

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity.

Kogelman LJ, Zhernakova DV, Westra HJ, Cirera S, Fredholm M, Franke L, Kadarmideen HN.

Genome Med. 2015 Oct 20;7:105. doi: 10.1186/s13073-015-0229-0.

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