AlzBase: An Integrative Database for Gene Dysregulation in Alzheimer’s Disease

WGCNA: an R package for weighted correlation network analysis

Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works

Conducting Meta-Analyses in <i>R</i> with the <b>metafor</b> Package

Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464 · see citing works

ggplot2: Elegant Graphics for Data Analysis

Ginestet, C. E. (2011) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00676_9.x · Cited by 14,484 · see citing works

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603 · see citing works

The igraph software package for complex network research

Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078 · see citing works

Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease

(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · see citing works

Controlling the false discovery rate in behavior genetics research

Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., & Golani, I. (2001) · Behavioural Brain Research · doi:10.1016/s0166-4328(01)00297-2 · Cited by 4,146 · see citing works

Reshaping Data with the reshape Package

Wickham, H. (2007) · Journal of Statistical Software · doi:10.18637/jss.v021.i12 · Cited by 3,167 · see citing works

An anatomically comprehensive atlas of the adult human brain transcriptome

Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng, L., Miller, J. A., van de Lagemaat, L. N., Smith, K. A., Ebbert, A., Riley, Z. L., et al. (2012) · Nature · doi:10.1038/nature11405 · Cited by 3,083 · see citing works

The NHGRI GWAS Catalog, a curated resource of SNP-trait associations

Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707 · see citing works

Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology

Voineagu, I., Wang, X., Johnston, P., Lowe, J. K., Tian, Y., Horvath, S., Mill, J., Cantor, R. M., Blencowe, B. J., & Geschwind, D. H. (2011) · Nature · doi:10.1038/nature10110 · Cited by 1,874 · cited 1× · see citing works

Gene regulation and DNA damage in the ageing human brain

Lu, T., Pan, Y., Kao, S.-Y., Li, C., Kohane, I., Chan, J., & Yankner, B. A. (2004) · Nature · doi:10.1038/nature02661 · Cited by 1,874 · see citing works

Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database

Bertram, L., McQueen, M. B., Mullin, K., Blacker, D., & Tanzi, R. E. (2007) · Nature Genetics · doi:10.1038/ng1934 · Cited by 1,857 · see citing works

Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease

Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657 · see citing works

Incipient Alzheimer's disease: Microarray correlation analyses reveal major transcriptional and tumor suppressor responses

Blalock, E. M., Geddes, J. W., Chen, K. C., Porter, N. M., Markesbery, W. R., & Landfield, P. W. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308512100 · Cited by 1,025 · see citing works

PGC-1 α, A Potential Therapeutic Target for Early Intervention in Parkinson’s Disease

Zheng, B., Liao, Z., Locascio, J. J., Lesniak, K. A., Roderick, S. S., Watt, M. L., Eklund, A. C., Zhang‐James, Y., Kim, P. D., Hauser, M. A., et al. (2010) · Science Translational Medicine · doi:10.1126/scitranslmed.3001059 · Cited by 854 · see citing works

Regional and cellular gene expression changes in human Huntington's disease brain

Hodges, A., Strand, A. D., Aragaki, A. K., Kuhn, A., Sengstag, T., Hughes, G., Elliston, L. A., Hartog, C., Goldstein, D. R., Thu, D., et al. (2006) · Human Molecular Genetics · doi:10.1093/hmg/ddl013 · Cited by 804 · see citing works

Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain

Gibbs, J. R., van der Brug, M. P., Hernandez, D. G., Traynor, B. J., Nalls, M. A., Lai, S.-L., Arepalli, S., Dillman, A., Rafferty, I. P., Troncoso, J., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000952 · Cited by 769 · see citing works

RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis

Hong, F., Rainer Breitling, A., McEntee, C., Wittner, B. S., Nemhauser, J. L., & Chory, J. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl476 · Cited by 753 · see citing works

Temporal dynamics and genetic control of transcription in the human prefrontal cortex

Colantuoni, C., Lipska, B. K., Ye, T., Hyde, T. M., Tao, R., Leek, J. T., Colantuoni, E. A., Elkahloun, A. G., Herman, M. M., Weinberger, D. R., et al. (2011) · Nature · doi:10.1038/nature10524 · Cited by 685 · see citing works

Gene expression changes in the course of normal brain aging are sexually dimorphic

Berchtold, N. C., Cribbs, D. H., Coleman, P. D., Rogers, J., Head, E., Kim, R., Beach, T., Miller, C., Troncoso, J., Trojanowski, J. Q., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806883105 · Cited by 626 · see citing works

Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons

Liang, W. S., Reiman, E. M., Valla, J., Dunckley, T., Beach, T. G., Grover, A., Niedzielko, T. L., Schneider, L. E., Mastroeni, D., Caselli, R., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709259105 · Cited by 585 · see citing works

A liver-derived secretory protein, selenoprotein P, causes insulin resistance

Misu, H., Takamura, T., Takayama, H., Hayashi, H., Matsuzawa-Nagata, N., Kurita, S., Ishikura, K., Ando, H., Takeshita, Y., Ota, T., et al. (2010) · Cell Metabolism · doi:10.1016/j.cmet.2010.09.015 · Cited by 583 · see citing works

Molecular markers of early Parkinson's disease based on gene expression in blood

Scherzer, C. R., Eklund, A. C., Morse, L. J., Liao, Z., Locascio, J. J., Fefer, D., Schwarzschild, M. A., Schlossmacher, M. G., Hauser, M. A., Vance, J. M., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610204104 · Cited by 511 · see citing works

The Comparative Toxicogenomics Database: update 2013

Davis, A. P., Grondin, C., Johnson, R. J., Lay, J. M., Lennon-Hopkins, K., Saraceni-Richards, C. A., Sciaky, D., King, B. L., Rosenstein, M. C., Wiegers, T. C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks994 · Cited by 497 · see citing works

Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis

Iwamoto, K., Bundo, M., & Kato, T. (2004) · Human Molecular Genetics · doi:10.1093/hmg/ddi022 · Cited by 496 · see citing works

Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease

Cruchaga, C., Karch, C. M., Jin, S. C., Benitez, B. A., Cai, Y., Guerreiro, R., Harari, O., Norton, J., Budde, J., Bertelsen, S., et al. (2014) · Nature · doi:10.1038/nature12825 · Cited by 461 · see citing works

Mutual information functions versus correlation functions

Li, W. (1990) · Journal of Statistical Physics · doi:10.1007/bf01025996 · Cited by 435 · see citing works

Isogenic Human iPSC Parkinson’s Model Shows Nitrosative Stress-Induced Dysfunction in MEF2-PGC1α Transcription

Ryan, S. D., Dolatabadi, N., Chan, S. F., Zhang, X., Akhtar, M. W., Parker, J., Soldner, F., Sunico, C. R., Nagar, S., Talantova, M. V., et al. (2013) · Cell · doi:10.1016/j.cell.2013.11.009 · Cited by 430 · see citing works

A Genomic Pathway Approach to a Complex Disease: Axon Guidance and Parkinson Disease

Lesnick, T. G., Papapetropoulos, S., Mash, D. C., ffrench‐Mullen, J. M. H., Shehadeh, L. A., de Andrade, M., Henley, J. R., Rocca, W. A., Ahlskog, J. E., & Maraganore, D. M. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030098 · Cited by 414 · see citing works

Entropy inference and the James-Stein estimator, with application to nonlinear gene association networksPreprint

Hausser, J., & Strimmer, K. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0811.3579 · Cited by 363 · see citing works

Genome-Wide Association Meta-analysis of Neuropathologic Features of Alzheimer's Disease and Related Dementias

Beecham, G. W., Hamilton, K., Naj, A. C., Martin, E. R., Huentelman, M., Myers, A. J., Corneveaux, J. J., Hardy, J., Vonsattel, J.-P., Younkin, S. G., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004606 · Cited by 343 · see citing works

Molecular characterization of bipolar disorder by comparing gene expression profiles of postmortem brains of major mental disorders

Iwamoto, K., Kakiuchi, C., Bundo, M., Ikeda, K., & Kato, T. (2004) · Molecular Psychiatry · doi:10.1038/sj.mp.4001437 · Cited by 331 · see citing works

Joint Genome-Wide Profiling of miRNA and mRNA Expression in Alzheimer's Disease Cortex Reveals Altered miRNA Regulation

Nunez-Iglesias, J., Liu, C.-C., Morgan, T. E., Finch, C. E., & Zhou, X. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0008898 · Cited by 320 · see citing works

Genetic Control of Human Brain Transcript Expression in Alzheimer Disease

Webster, J. A., Gibbs, J. R., Clarke, J., Ray, M., Zhang, W., Holmans, P., Rohrer, K., Zhao, A., Marlowe, L., Kaleem, M., et al. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.03.011 · Cited by 304 · see citing works

MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain

Somel, M., Guo, S., Fu, N., Yan, Z., Hu, H. Y., Xu, Y., Yuan, Y., Ning, Z., Hu, Y., Menzel, C., et al. (2010) · Genome Research · doi:10.1101/gr.106849.110 · Cited by 285 · see citing works

Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson’s disease

Moran, L. B., Duke, D. C., Deprez, M., Dexter, D. T., Pearce, R. K. B., & Graeber, M. B. (2005) · Neurogenetics · doi:10.1007/s10048-005-0020-2 · Cited by 245 · see citing works

Gene expression analysis of bipolar disorder reveals downregulation of the ubiquitin cycle and alterations in synaptic genes

Ryan, M. M., Lockstone, H. E., Huffaker, S. J., Wayland, M. T., Webster, M. J., & Bahn, S. (2006) · Molecular Psychiatry · doi:10.1038/sj.mp.4001875 · Cited by 238 · see citing works

Gene Expression in Peripheral Blood Mononuclear Cells from Children with Diabetes

Kaizer, E., Glaser, C., Chaussabel, D., Banchereau, J., Pascual, V., & White, P. C. (2007) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2007-0979 · Cited by 219 · see citing works

Analysis of gene expression in two large schizophrenia cohorts identifies multiple changes associated with nerve terminal function

Maycox, P. R., Kelly, F., Taylor, A. R., Bates, S., Reid, J. L., Logendra, R., Barnes, M. R., Larminie, C. G. C., Jones, N. A., Lennon, M. G., et al. (2009) · Molecular Psychiatry · doi:10.1038/mp.2009.18 · Cited by 199 · see citing works

Gene expression correlates of neurofibrillary tangles in Alzheimer's disease

Dunckley, T., Beach, T. G., Ramsey, K. E., Grover, A., Mastroeni, D., Walker, D. G., LaFleur, B. J., Coon, K. D., Brown, K. M., Caselli, R., et al. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2005.08.013 · Cited by 177 · see citing works

Brain Transcriptional and Epigenetic Associations with Autism

Ginsberg, M. R., Rubin, R. A., Falcone, T., Ting, A. H., & Natowicz, M. R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044736 · Cited by 164 · see citing works

Gene-chip studies of adipogenesis-regulated microRNAs in mouse primary adipocytes and human obesity

Keller, P., Gburcik, V., Petrovič, N., Gallagher, I. J., Nedergaard, J., Cannon, B., & Timmons, J. A. (2011) · BMC Endocrine Disorders · doi:10.1186/1472-6823-11-7 · Cited by 139 · see citing works

Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain

Hernandez, D. G., Nalls, M. A., Moore, M., Chong, S., Dillman, A., Trabzuni, D., Gibbs, J. R., Ryten, M., Arepalli, S., Weale, M. E., et al. (2012) · Neurobiology of Disease · doi:10.1016/j.nbd.2012.03.020 · Cited by 128 · see citing works

Transcriptome analysis of synaptoneurosomes identifies neuroplasticity genes overexpressed in incipient Alzheimer's disease

Williams, C., Mehrian Shai, R., Wu, Y., Hsu, Y.-H., Sitzer, T., Spann, B., McCleary, C., Mo, Y., & Miller, C. A. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004936 · Cited by 116 · see citing works

In Vivo Analysis of MEF2 Transcription Factors in Synapse Regulation and Neuronal Survival

Akhtar, M. W., Kim, M.-S., Adachi, M., Morris, M. J., Qi, X., Richardson, J. A., Bassel‐Duby, R., Olson, E. N., Kavalali, E. T., & Monteggia, L. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034863 · Cited by 113 · see citing works

Physical activity is the key determinant of skeletal muscle mitochondrial function in type 2 diabetes

van Tienen, F. H. J., Praet, S. F. E., de Feyter, H. M., van den Broek, N. M., Lindsey, P. J., Schoonderwoerd, K. G. C., de Coo, I. F. M., Nicolay, K., Prompers, J. J., Smeets, H. J. M., et al. (2012) · The Journal of Clinical Endocrinology &amp; Metabolism · doi:10.1210/jc.2011-3454 · Cited by 111 · cited 1× · see citing works

Increased SRF transcriptional activity in human and mouse skeletal muscle is a signature of insulin resistance

Jin, W., Goldfine, A. B., Boes, T., Henry, R. R., Ciaraldi, T. P., Kim, E. Y., Şanlı, M. E., Fitzpatrick, C., Sen, A., Shah, A. M., et al. (2011) · Journal of Clinical Investigation · doi:10.1172/jci41940 · Cited by 106 · see citing works

The Cerebral Microvasculature in Schizophrenia: A Laser Capture Microdissection Study

Harris, L. W., Wayland, M. T., Lan, M. J., Ryan, M., Giger, T., Lockstone, H. E., Wüthrich, I., Mimmack, M. L., Wang, L., Kotter, M., et al. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003964 · Cited by 104 · see citing works

Evidence for a Novel Late-Onset Alzheimer Disease Locus on Chromosome 19p13.2

Wijsman, E. M., Daw, E. W., Yu, C. E., Payami, H., Steinbart, E. J., Nochlin, D., Conlon, E. M., Bird, T. D., & Schellenberg, G. D. (2004) · The American Journal of Human Genetics · doi:10.1086/423393 · Cited by 95 · see citing works

Concerted Perturbation Observed in a Hub Network in Alzheimer’s Disease

Liang, D., Han, G., Feng, X., Sun, J., Duan, Y., & Lei, H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040498 · Cited by 93 · see citing works

Genetic Determinants of Disease Progression in Alzheimer's Disease

Wang, X., López, O. L., Sweet, R. A., Becker, J. T., DeKosky, S. T., Barmada, M. M., Demirci, F. Y., & Kamboh, M. I. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-140729 · Cited by 68 · see citing works

Refining the phenotype associated with MEF2C point mutations

Bienvenu, T., Diebold, B., Chelly, J., & Isidor, B. (2012) · Neurogenetics · doi:10.1007/s10048-012-0344-7 · Cited by 60 · see citing works

Beyond amyloid: Getting real about nonamyloid targets in Alzheimer's disease

Herrup, K., Carrillo, M. C., Schenk, D., Cacace, A. M., DeSanti, S., Fremeau, R. T., Bhat, R. V., Glicksman, M. A., May, P. C., Swerdlow, R. H., et al. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.01.017 · Cited by 54 · see citing works

Distinctive RNA Expression Profiles in Blood Associated With Alzheimer Disease After Accounting for White Matter Hyperintensities

Bai, Z., Stamova, B. S., Xu, H., Ander, B. P., Wang, J., Jickling, G. C., Zhan, X., Liu, D., Han, G., Jin, L., et al. (2014) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000022 · Cited by 52 · see citing works

Down-Regulation of Energy Metabolism in Alzheimer's Disease is a Protective Response of Neurons to the Microenvironment

Sun, J., Feng, X., Liang, D., Duan, Y., & Lei, H. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-111313 · Cited by 49 · see citing works

A genome‐wide linkage analysis of dementia in the Amish

Hahs, D. W., McCauley, J. L., Crunk, A. E., McFarland, L. L., Gaskell, P. C., Jiang, L., Slifer, S. H., Vance, J. M., Scott, W. K., Welsh-Bohmer, K. A., et al. (2006) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.30257 · Cited by 47 · see citing works

Hidden Risk Genes with High-Order Intragenic Epistasis in Alzheimer's Disease

Sun, J., Song, F., Wang, J., Han, G., Bai, Z., Xie, B., Feng, X., Jia, J., Duan, Y., & Lei, H. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-140054 · Cited by 40 · see citing works

Characteristic Transformation of Blood Transcriptome in Alzheimer's Disease

Han, G., Wang, J., Zeng, F., Feng, X., Yu, J. M., Cao, H., Yi, X., Zhou, H., Jin, L., Duan, Y., et al. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-121963 · Cited by 40 · see citing works

Global gene expression profiling displays a network of dysregulated genes in non-atherosclerotic arterial tissue from patients with type 2 diabetes

Skov, V., Knudsen, S., Olesen, M., Hansen, M. L., & Rasmussen, L. M. (2012) · Cardiovascular Diabetology · doi:10.1186/1475-2840-11-15 · Cited by 37 · see citing works

PLD3 in Alzheimer’s Disease

Wang, J., Yu, J., & Tan, L. (2014) · Molecular Neurobiology · doi:10.1007/s12035-014-8779-5 · Cited by 33 · see citing works

Genomics in Neurological Disorders

Han, G., Sun, J., Wang, J., Bai, Z., Song, F., & Lei, H. (2014) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2014.07.002 · Cited by 26 · see citing works

Analysis of gene expression data using a linear mixed model/finite mixture model approach: application to regional differences in the human brain

Trabzuni, D., & Thomson, P. C. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu088 · Cited by 25 · see citing works

Amyloid and Alzheimer’s disease

Lei, H. (2010) · Protein & Cell · doi:10.1007/s13238-010-0046-6 · Cited by 19 · see citing works

Robust Gene Dysregulation in Alzheimer's Disease Brains

Feng, X., Bai, Z., Wang, J., Xie, B., Sun, J., Han, G., Song, F., Crack, P. J., Duan, Y., & Lei, H. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-140147 · Cited by 16 · see citing works

iBIG: An Integrative Network Tool for Supporting Human Disease Mechanism Studies

Sun, J., Pan, Y., Feng, X., Zhang, H., Duan, Y., & Lei, H. (2013) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2012.08.007 · Cited by 8 · see citing works

Chromosome 19p in Alzheimer's Disease: When Genome Meets Transcriptome

Wang, J., Feng, X., Bai, Z., Jin, L., Duan, Y., & Lei, H. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-130917 · Cited by 7 · see citing works

Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works

Autophagy and Alzheimer’s Disease: From Molecular Mechanisms to Therapeutic Implications

Uddin, M. S., Stachowiak, A., Mamun, A. A., Tzvetkov, N. T., Takeda, S., Atanasov, A. G., Bergantin, L. B., Abdel‐Daim, M. M., & Stankiewicz, A. M. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00004 · Cited by 440 · see citing works

Role of Oxidative Damage in Alzheimer’s Disease and Neurodegeneration: From Pathogenic Mechanisms to Biomarker Discovery

Buccellato, F. R., D’Anca, M., Fenoglio, C., Scarpini, E., & Galimberti, D. (2021) · Antioxidants · doi:10.3390/antiox10091353 · Cited by 179 · see citing works

Biological Databases for Human Research

Zou, D., Ma, L., Yu, J. M., & Zhang, Z. (2015) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2015.01.006 · Cited by 118 · see citing works

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

Hu, Y.-S., Xin, J., Hu, Y. C., Zhang, L., & Wang, J. (2017) · Alzheimer s Research & Therapy · doi:10.1186/s13195-017-0252-z · Cited by 110 · see citing works

Defining early changes in Alzheimer’s disease from RNA sequencing of brain regions differentially affected by pathology

Guennewig, B., Lim, J., Marshall, L. L., McCorkindale, A. N., Paasila, P. J., Patrick, E., Kril, J. J., Halliday, G. M., Cooper, A. A., & Sutherland, G. T. (2021) · Scientific Reports · doi:10.1038/s41598-021-83872-z · Cited by 59 · see citing works

Prenatal exposure to bisphenol A alters the transcriptome-interactome profiles of genes associated with Alzheimer’s disease in the offspring hippocampus

Sukjamnong, S., Thongkorn, S., Kanlayaprasit, S., Saeliw, T., Hussem, K., Warayanon, W., Hu, V. W., Tencomnao, T., & Sarachana, T. (2020) · Scientific Reports · doi:10.1038/s41598-020-65229-0 · Cited by 56 · see citing works

Identification of key genes and pathways for Alzheimer’s disease via combined analysis of genome-wide expression profiling in the hippocampus

Wu, M., Fang, K.-C., Wang, W., Lin, W., Guo, L., & Wang, J. (2019) · Biophysics Reports · doi:10.1007/s41048-019-0086-2 · Cited by 40 · see citing works

Meta-analysis of genetic association with diagnosed Alzheimer’s disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing

Kunkle, B., Grenier-Boley, B., Sims, R., Bis, J., Naj, A., Boland, A., Vronskaya, M., van der Lee, S., Amlie-Wolf, A., Bellenguez, C., et al. (2018) · doi:10.1101/294629 · Cited by 35 · see citing works

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

Dragomir, A., Vrahatis, A. G., & Bezerianos, A. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2863202 · Cited by 34 · see citing works

Mitochondrial ubiquitin ligase alleviates Alzheimer’s disease pathology via blocking the toxic amyloid-β oligomer generation

Takeda, K., Uda, A., Mitsubori, M., Nagashima, S., Iwasaki, H., Ito, N., Shiiba, I., Ishido, S., Matsuoka, M., Inatome, R., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01720-2 · Cited by 32 · see citing works

Identifying key genetic variants in Alzheimer’s disease progression using Graph Convolutional Networks (GCN) and biological impact analysis

Hamed, B. A., Farghaly, H. M., Ahmed, O. A., & El‐Hafeez, T. A. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01228-0 · Cited by 32 · see citing works

Candidate Genes and MiRNAs Linked to the Inverse Relationship Between Cancer and Alzheimer’s Disease: Insights From Data Mining and Enrichment Analysis

Battaglia, C., Venturin, M., Šojić, A., Jesuthasan, N., Orro, A., Spinelli, R., Musicco, M., Bellis, G. D., & Adorni, F. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00846 · Cited by 26 · see citing works

Interdisciplinary Research in Alzheimer’s Disease and the Roles International Societies Can Play

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Chen, H., Zhou, Y., Liu, Y., Zhang, P., & Chen, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad237 · Cited by 5 · see citing works

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Yan, R. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735170868.793558625 · Cited by 2 · see citing works

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Yan, Q. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-804328-8.00012-7

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