The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways
Open accessAbstract
Because mouse models play a crucial role in biomedical research related to the human nervous system, understanding the similarities and differences between mouse and human brain is of fundamental importance. Studies comparing transcription in human and mouse have come to varied conclusions, in part because of their relatively small sample sizes or underpowered methodologies. To better characterize gene expression differences between mouse and human, we took a systems-biology approach by using weighted gene coexpression network analysis on more than 1,000 microarrays from brain. We find that global network properties of the brain transcriptome are highly preserved between species. Furthermore, all modules of highly coexpressed genes identified in mouse were identified in human, with those related to conserved cellular functions showing the strongest between-species preservation. Modules corresponding to glial and neuronal cells were sufficiently preserved between mouse and human to permit identification of cross species cell-class marker genes. We also identify several robust human-specific modules, including one strongly correlated with measures of Alzheimer disease progression across multiple data sets, whose hubs are poorly-characterized genes likely involved in Alzheimer disease. We present multiple lines of evidence suggesting links between neurodegenerative disease and glial cell types in human, including human-specific correlation of presenilin-1 with oligodendrocyte markers, and significant enrichment for known neurodegenerative disease genes in microglial modules. Together, this work identifies convergent and divergent pathways in mouse and human, and provides a systematic framework that will be useful for understanding the applicability of mouse models for human brain disorders.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This work also found major differences in dementia susceptibility genes between humans and mice, potentially identifying why some mouse models might not recapitulate human neuropathology.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [150]
In the citing paper
“They subsequently150 incorporated more than 1,000 microarrays from mouse models of Alzheimer disease and human patients with Alzheimer disease from public databases to reproduce and extend these results, identifying additional co-expression modules that are related to mitochondrial dysfunction and synaptic plasticity.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [150]
In the citing paper
“Applied to the entire 911 sample set from Brain 1, genes were grouped into well-defined co-expression modules with specific anatomical distributions (Fig. 3a, b), consistent with previous studies in brain tissues18, 19.”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [19]
In the citing paper
“Modules corresponding to broad neural cell types also showed highly significant preservation compared to a previous study using human brain samples (ref. 19 and data not shown).”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [19]
In the citing paper
“a number of important studies have compared gene expression in microglia from mice and humans, and while they have shown a significant overlap between the transcriptomes of the two species, they have also seen a number of genes are expressed selectively more abundantly in human microglia”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Miller et al., 2010
In the citing paper
“a meta-analysis of microarray studies which showed better preservation of neuronal gene co-expression compared to glial and disease-related genes36”
From Canonical genetic signatures of the adult human brain (2015) · cites this paper as [36]
In the citing paper
“Microarray studies have also revealed distinct gene expression profiles and coexpression networks associated with different human brain regions, major cell types, and cellular processes (Johnson et al., 2009; Miller et al., 2010; Oldham et al., 2006; Oldham et al., 2008).”
From Large-Scale Cellular-Resolution Gene Profiling in Human Neocortex Reveals Species-Specific Molecular Signatures (2012) · cites this paper as Miller et al., 2010
Citations
Cites
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
Self-citationGene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Self-citationGenome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Triple-Transgenic Model of Alzheimer’s Disease with Plaques and Tangles
Oddo, S., Caccamo, A., Shepherd, J. D., Murphy, M. P., Golde, T. E., Kayed, R., Metherate, R., Mattson, M. P., Akbari, Y., & LaFerla, F. M. (2003) · Neuron · doi:10.1016/s0896-6273(03)00434-3 · Cited by 4,126
A gene atlas of the mouse and human protein-encoding transcriptomes
Su, A. I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K. A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400782101 · Cited by 3,475
Human Protein Reference Database--2009 update
Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,252
Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129
King, M.-C., & Wilson, A. C. (1975) · Science · doi:10.1126/science.1090005 · Cited by 2,815
Cited by
Sorted by citations
Items 201–300 of 477
Critical thinking on amyloid-beta-targeted therapy: challenges and perspectives
Sun, B., Chen, Y., Fan, D., Zhu, C., Zeng, F., & Wang, Y. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1810-y · Cited by 27
Proteogenomics of the human hippocampus: The road ahead
Kang, M., Byun, K., Kim, J. H., Park, N. H., Heinsen, H., Ravid, R., Steinbusch, H. W. M., Lee, B., & Park, Y. M. (2015) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2015.02.010 · Cited by 27
Tan, P. P. C., French, L., & Pavlidis, P. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00005 · Cited by 25
Interhemispheric gene expression differences in the cerebral cortex of humans and macaque monkeys
Muntané, G., Santpere, G., Verendeev, A., Seeley, W. W., Jacobs, B., Hopkins, W. D., Navarro, A., & Sherwood, C. C. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1401-7 · Cited by 25
Co-expression of key gene modules and pathways of human breast cancer cell lines
Wu, Y., Liu, F., Luo, S., Yin, X., He, D., Liu, J., Yue, Z.-H., & Song, J. (2019) · Bioscience Reports · doi:10.1042/bsr20181925 · Cited by 25
CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types
Nelms, B. D., Waldron, L., Barrera, L. A., Weflen, A. W., Goettel, J. A., Guo, G., Montgomery, R. K., Neutra, M. R., Breault, D. T., Snapper, S. B., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1062-5 · Cited by 24
Gene Network and Proteomic Analyses of Cardiac Responses to Pathological and Physiological Stress
Drozdov, I. A., Didangelos, A., Yin, X., Zampetaki, A., Abonnenc, M., Murdoch, C. E., Zhang, M., Ouzounis, C., Mayr, M., Tsoka, S., et al. (2013) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000063 · Cited by 24
Shi, Q., Li, L., Zhang, X., Luo, J., Li, X., Zhai, L., Lixia, H., & Zhang, Y. (2017) · Molecules · doi:10.3390/molecules22081364 · Cited by 24
Molecular network analysis enhances understanding of the biology of mental disorders
Grennan, K. S., Chen, C., Gershon, E. S., & Liu, C. (2014) · BioEssays · doi:10.1002/bies.201300147 · Cited by 24
Gene Expression Signatures of Sporadic ALS Motor Neuron PopulationsPreprint
Batra, R., Hutt, K. R., Vu, A. Q., Rabin, S. J., Baughn, M. W., Libby, R. T., Hoon, S. S., Ravits, J., & Yeo, G. W.-M. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/038448 · Cited by 25
Bao, S., Wang, X., Li, M., Gao, Z., Zheng, D., Shen, D., & Liu, L. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.835549 · Cited by 23
Zhou, Y., Xu, J., Liu, Y., Li, J., Chang, C., & Xu, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094868 · Cited by 23
Cross-species gene modules emerge from a systems biology approach to osteoarthritis
Mueller, A. J., Canty‐Laird, E. G., Clegg, P., & Tew, S. R. (2017) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0014-3 · Cited by 22
Designing, Performing, and Interpreting a Microarray-Based Gene Expression Study
Coppola, G. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-328-8_28 · Cited by 23
Augustin, R., Lichtenthaler, S. F., Greeff, M., Hansen, J., Wurst, W., & Trümbach, D. (2011) · International Journal of Alzheimer s Disease · doi:10.4061/2011/154325 · Cited by 23
Puniya, B. L., Kulshreshtha, D., Verma, S. P., Kumar, S., & Ramachandran, S. (2013) · Molecular BioSystems · doi:10.1039/c3mb70278b · Cited by 21
Klein, K., Oualkacha, K., Lafond, M.-H., Bhatnagar, S., Tonin, P. N., & Greenwood, C. M. T. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00137 · Cited by 21
Cross-Species Gene Expression Analysis Reveals Gene Modules Implicated in Human Osteosarcoma
Jin, Z., Liu, S., Zhu, P., Tang, M., Wang, Y., Tian, Y., Li, D., Zhu, X., Yan, D., & Zhu, Z. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00697 · Cited by 21
Kumar, A., Tiwari, P., Saxena, A., Purwar, N., Wahi, N., Sharma, B., & Mathur, S. K. (2020) · Biomolecules · doi:10.3390/biom10091230 · Cited by 21
Mukherjee, S. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e08518 · Cited by 23
Subudhi, A. K., Boopathi, P. A., Pandey, I., Kaur, R., Middha, S., Acharya, J., Kochar, S. K., Kochar, D. K., & Das, A. (2015) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2015.08.007 · Cited by 21
Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer
Ivliev, A. E., Hoen, P. A. C. ’., Borisevich, D., Nikolsky, Y., & Sergeeva, M. G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165059 · Cited by 21
Blood and brain gene expression signatures of chronic intermittent ethanol consumption in mice
Ferguson, L. B., Roberts, A. J., Mayfield, R. D., & Messing, R. O. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009800 · Cited by 22
GERDH : an interactive multi‐omics database for cross‐species data mining in horticultural crops
Cheng, H., Zhang, H., Song, J., Jiang, J., Chen, S., Chen, F., & Wang, L. (2023) · The Plant Journal · doi:10.1111/tpj.16350 · Cited by 20
Howles, P. A., Gebbie, L. K., Collings, D. A., Varsani, A., Broad, R. C., Ohms, S., Birch, R. J., Cork, A. H., Arioli, T., & Williamson, R. E. (2016) · Plant Molecular Biology · doi:10.1007/s11103-016-0428-0 · Cited by 20
Turning omics data into therapeutic insights
Kedaigle, A. J., & Fraenkel, E. (2018) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2018.08.006 · Cited by 20
Systems biology and gene networks in Alzheimer’s disease
Wang, Z.-T., Tan, C., Tan, L., & Yu, J. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.11.007 · Cited by 20
Chen, Q., Lu, Y.-Y., Zhang, G.-B., Song, Y., Zhou, Q., Zhang, H., Zhang, W., Tang, X., & Su, S.-B. (2013) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2013/324636 · Cited by 20
Mechanosensitivity of Human Oligodendrocytes
Espinosa-Hoyos, D., Burstein, S. R., Cha, J., Jain, T., Nijsure, M. P., Jagielska, A., Fossati, V., & Vliet, K. J. V. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.00222 · Cited by 19
miR-155-5p regulates hypoxia-induced pulmonary artery smooth muscle cell function by targeting PYGL
Wang, G., Tao, X., & Peng, L. (2022) · Bioengineered · doi:10.1080/21655979.2022.2079304 · Cited by 19
Molecular alterations in areas generating fast ripples in an animal model of temporal lobe epilepsy
Winden, K. D., Bragin, A., Engel, J., & Geschwind, D. H. (2015) · Neurobiology of Disease · doi:10.1016/j.nbd.2015.02.011 · Cited by 19
Self-citationChen, Q., Lu, Y.-Y., Zhang, G.-B., Song, Y., Zhou, Q., Zhang, H., Zhang, W., & Su, S.-B. (2013) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2013/945245 · Cited by 19
Pan, Q., Long, X., Song, L., Zhao, D., Li, X., Li, D., Li, M., Zhou, J., Tang, X., Ren, H., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.9555 · Cited by 19
Voigt, A., Nowick, K., & Almaas, E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005739 · Cited by 19
Yamaguchi‐Kabata, Y., Morihara, T., Ohara, T., Ninomiya, T., Takahashi, A., Akatsu, H., Hashizume, Y., Hayashi, N., Shigemizu, D., Boroevich, K. A., et al. (2018) · Human Genetics · doi:10.1007/s00439-018-1906-z · Cited by 20
Yuan, J., Tan, L., Yin, Z., Tao, K., Wang, G., Shi, W., & Gao, J. (2018) · Oncology Reports · doi:10.3892/or.2018.6794 · Cited by 20
Lee, M. J., Wang, C., Carroll, M. J., Brubaker, D. K., Hyman, B. T., & Lauffenburger, D. A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.727784 · Cited by 19
Bragg, R. M., Coffey, S. R., Weston, R. M., Ament, S. A., Cantle, J. P., Minnig, S., Funk, C. C., Shuttleworth, D. D., Woods, E. L., Sullivan, B. R., et al. (2017) · Scientific Reports · doi:10.1038/srep41570 · Cited by 18
Understanding the circuit basis of cognitive functions using mouse models
Nakajima, M., & Schmitt, L. I. (2019) · Neuroscience Research · doi:10.1016/j.neures.2019.12.009 · Cited by 19
Mukherjee, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-67753-5 · Cited by 18
Liu, J., Liu, S., & Yang, X. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.542063 · Cited by 18
Ma, X., Su, B., Tian, Y., Backenstose, N. J. C., Ye, Z., Moss, A. G., Yên, D. T., Wang, X., & Dunham, R. A. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21155535 · Cited by 18
Systemic neurotransmitter responses to clinically approved and experimental neuropsychiatric drugs
Noori, H. R., Mervin, L., Bokharaie, V. S., Durmus, Ö., Egenrieder, L., Fritze, S., Gruhlke, B., Reinhardt, G., Schabel, H.-H., Staudenmaier, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07239-1 · Cited by 17
Pathania, S., Bagler, G., & Ahuja, P. S. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01229 · Cited by 17
Mariani, J. N., Mansky, B., Madsen, P. M., Salinas, D., Kesmen, D., Huynh, N. P. T., Kuypers, N. J., Kesel, E. R., Bates, J., Payne, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48118-2 · Cited by 16
Multi-study Integration of Brain Cancer Transcriptomes Reveals Organ-Level Molecular Signatures
Sung, J., Kim, P., Ma, S., Funk, C. C., Magis, A. T., Wang, Y., Hood, L., Geman, D., & Price, N. D. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003148 · Cited by 16
Co-expression network analysis identifies transcriptional modules in the mouse liver
Liu, W., & Ye, H. (2014) · Molecular Genetics and Genomics · doi:10.1007/s00438-014-0859-8 · Cited by 17
Zheng, T., Ni, Y., Li, J., Chow, B., & Panagiotou, G. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00753 · Cited by 16
Nontransgenic Guinea Pig Strains Exhibit Hallmarks of Human Brain Aging and Alzheimer’s Disease
Wahl, D., Moreno, J. A., Santangelo, K. S., Zhang, Q. Y., Afzali, M. F., Walsh, M., Musci, R. V., Cavalier, A. N., Hamilton, K. L., & LaRocca, T. J. (2022) · The Journals of Gerontology Series A · doi:10.1093/gerona/glac073 · Cited by 17
Gene coexpression networks reveal key drivers of phenotypic divergence in porcine muscle
Zhao, X. L., Liu, Z., & Liu, Q. (2015) · BMC Genomics · doi:10.1186/s12864-015-1238-5 · Cited by 17
A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor
LoVerso, P. R., & Cui, F. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s30884 · Cited by 17
Codoni, V., Blum, Y., Civelek, M., Proust, C., Franzén, O., Björkegren, J., Goff, W. L., Cambien, F., Lusis, A. J., & Trégouët, D. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.033894 · Cited by 16
Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer
Tříska, M., Ivliev, A. E., Nikolsky, Y., & Tatarinova, T. V. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7027-8_11 · Cited by 15
Joshi, A., Beck, Y., & Michoel, T. (2015) · Journal of Computational Biology · doi:10.1089/cmb.2014.0290 · Cited by 15
Age- and Nicotine-Associated Gene Expression Changes in the Hippocampus of APP/PS1 Mice
Yang, J., Long, Y., Xu, D., Zhu, B., Deng, X., Yan, Z., Sun, F., & Chen, G.-J. (2019) · Journal of Molecular Neuroscience · doi:10.1007/s12031-019-01389-7 · Cited by 15
Decoding dendritic cell function through module and network analysis
Pandey, G. S., Cohain, A. T., Miller, J. C., & Mérad, M. (2012) · Journal of Immunological Methods · doi:10.1016/j.jim.2012.09.012 · Cited by 16
Self-citation[Mercury poisoning caused by a homeopathic drug].
Montoya-Cabrera, M. A., Rubio-rodriguez, S., Velázquez-Gonzalez, E., & Montoya, S. A. (1991) · PubMed · Cited by 15
Chameh, H. M., Falby, M. R., Movahed, M., Arbabi, K., Rich, S., Zhang, L., Lefebvre, J., Tripathy, S. J., Pittà, M. D., & Valiante, T. A. (2023) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2023.1250834 · Cited by 15
Expression profiles of the autism-related SHANK proteins in the human brain
Woelfle, S., Pedro, M. T., Wagner, J., Schön, M., & Boeckers, T. M. (2023) · BMC Biology · doi:10.1186/s12915-023-01712-0 · Cited by 15
Wu, D., Ye, L., Li, Y., Sun, Y., Shao, Y., Chen, C., Zhu, Z., Li, Z., Wang, L., Irwin, D. M., et al. (2015) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjv043 · Cited by 14
Grange, P., Hawrylycz, M., & Mitra, P. P. (2013) · Quantitative Biology · doi:10.1007/s40484-013-0011-5 · Cited by 14
González‐Peñas, J., Costas, J., Villamayor, M. J. G., & Xu, B. (2018) · Neurobiology of Disease · doi:10.1016/j.nbd.2018.10.013 · Cited by 16
Fogel, B. L., & Geschwind, D. H. (2012) · Elsevier eBooks · doi:10.1016/b978-1-4377-0434-1.00040-2 · Cited by 14
Self-citationVitale, P., Librizzi, F., Vaiana, A. C., Capuana, E., Pezzoli, M., Shi, Y., Romani, A., Migliore, M., & Migliore, R. (2023) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2023.1281932 · Cited by 14
Characterization of Genetic Networks Associated with Alzheimer’s Disease
Zhang, B., Tran, L. M., Emilsson, V., & Zhu, J. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2627-5_28 · Cited by 14
Faner, R., Morrow, J. D., Casas-Recasens, S., Cloonan, S. M., Noell, G. G., López-Giraldo, A., Tal‐Singer, R., Miller, B. E., Silverman, E. K., Agustí, A. G. N., et al. (2019) · Respiratory Research · doi:10.1186/s12931-018-0965-y · Cited by 14
csuWGCNA: a combination of signed and unsigned WGCNA to capture negative correlations
Dai, R., Xia, Y., Liu, C., & Chen, C. (2018) · doi:10.1101/288225 · Cited by 13
Piro, R. M., Molineris, I., Ala, U., & Cunto, F. D. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023149 · Cited by 13
Li, H., Li, Q., Sun, S., Lei, P., Cai, X., & Shen, G. (2020) · Journal of Immunology Research · doi:10.1155/2020/3067273 · Cited by 14
Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma
Zhan, C., Wang, Z., Xu, C., Huang, X., Su, J., Chen, B., Wang, M., Qi, Z., & Bai, P. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.609865 · Cited by 13
Ray, S., & Bandyopadhyay, S. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2505300 · Cited by 13
Brenna, Ø., Furnes, M. W., Drozdov, I. A., Granlund, A. V. B., Flatberg, A., Sandvik, A. K., Zwiggelaar, R. T., Mårvik, R., Nordrum, I. S., Kidd, M. S., et al. (2013) · PLoS ONE · doi:10.1371/annotation/af20399c-993e-436c-ad06-e0d8a657a7b4 · Cited by 13
Muntané, G., Chillida, M., Aranda, S., Navarro, A., & Vilella, E. (2021) · Brain and Behavior · doi:10.1002/brb3.2309 · Cited by 12
Roll, L., Lessmann, K., Brüstle, O., & Faissner, A. (2022) · Cells · doi:10.3390/cells11050760 · Cited by 13
Hippocampal gene profiling: Toward a systems biology of the hippocampus
Valor, L. M., & Barco, Á. (2010) · Hippocampus · doi:10.1002/hipo.20888 · Cited by 12
Functional genomics of the brain: uncovering networks in the CNS using a systems approach
Konopka, G. (2010) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.139 · Cited by 12
Chen, Q., Lu, Y.-Y., Peng, J., Dong, S., Wei, B. K., Song, Y., Zhou, Q., Zhang, H., Liu, P., & Su, S.-B. (2015) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2015/238495 · Cited by 12
Investigating the Combinatory Effects of Biological Networks on Gene Co-expression
Zhang, C., Lee, S., Mardinoğlu, A., & Hua, Q. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00160 · Cited by 13
Dysregulation of Alternative Poly-adenylation as a Potential Player in Autism Spectrum Disorder
Szkop, K. J., Cooke, P. I. C., Humphries, J. A., Kalna, V., Moss, D. S., Schuster, E. F., & Nobeli, I. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00279 · Cited by 12
Luo, J., Xu, P., Cao, P., Wan, H., Lv, X., Xu, S., Wang, G., Cook, M. N., Jones, B. C., Lu, L., et al. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00102 · Cited by 12
Okada, D., Endo, S., Matsuda, H., Ogawa, S., Taniguchi, Y., Katsuta, T., Watanabe, T., & Iwaisaki, H. (2018) · Journal of Animal Science · doi:10.1093/jas/sky170 · Cited by 12
Jones, A. C., Anderson, D. R., Troy, N. M., Mallon, D., Hartmann, R., Serralha, M., Holt, B. J., Bosco, A., & Holt, P. G. (2020) · Allergy · doi:10.1111/all.14265 · Cited by 12
Blood Transcriptomic Biomarkers of Alzheimer's Disease Patients Treated with EHT 0202
Désiré, L., Blondiaux, E., Carrière, J., Haddad, R., Sol, O., Fehlbaum, P., Einstein, R., Zhou, W., & Pando, M. P. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-121501 · Cited by 12
Chung-Wei, Y., Chen, C., Chou, W., Lin, H.-C., Jong, Y., Tsai, L., & Chuang, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157426 · Cited by 12
Advances in Human Stem Cell-Derived Neuronal Cell Culturing and Analysis
Ylä‐Outinen, L., Tanskanen, J. M. A., Kapucu, F. E., Hyysalo, A., Hyttinen, J., & Narkilahti, S. (2019) · Advances in neurobiology · doi:10.1007/978-3-030-11135-9_13 · Cited by 13
Kara, E., Crimi, A., Wiedmer, A. E., Emmenegger, M., Manzoni, C., Bandrés‐Ciga, S., D’Sa, K., Reynolds, R. H., Botía, J. A., Losa, M., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109189 · Cited by 12
Chai, Z., Lyu, Y., Chen, Q., Wei, C.-H., Snyder, L. M., Weaver, V. C., Sebastian, A., Albert, I. U., Li, Q., Cantorna, M. T., et al. (2021) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2021.108814 · Cited by 11
Mammalian Models in Alzheimer’s Research: An Update
Sharma, H., Chang, K., Hulme, J., & An, S. S. A. (2023) · Cells · doi:10.3390/cells12202459 · Cited by 12
Tan, H., Lv, M., Tan, X., Su, G., Chang, R., & Yang, P. (2020) · Molecular Neurobiology · doi:10.1007/s12035-020-02024-y · Cited by 12
Neurogenetics in Peru: clinical, scientific and ethical perspectives
Cornejo‐Olivas, M., Espinoza-Huertas, K., Velit-Salazar, M. R., Véliz-Otani, D., Tirado-Hurtado, I., Inca‐Martinez, M., Silva-Paredes, G., Milla‐Neyra, K., Marca, V., Ortega, O., et al. (2015) · Journal of Community Genetics · doi:10.1007/s12687-015-0239-z · Cited by 11
A cross-species analysis method to analyze animal models' similarity to human's disease state
Yu, S., Zheng, L., Li, Y., Li, C., Ma, C., Li, Y., Li, X., & Hao, P. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-s3-s18 · Cited by 11
Application of Computational Biology to Decode Brain Transcriptomes
Li, J., & Wang, G. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.03.003 · Cited by 11
Wang, P., Zhao, D., Rockowitz, S., & Zheng, D. (2016) · Neurogenesis · doi:10.1080/23262133.2016.1231495 · Cited by 12
Orda, M. A., Fowler, P. M. P., & Tayo, L. L. (2024) · Biology · doi:10.3390/biology13040206 · Cited by 10
Mueller, A. J., Peffers, M. J., Proctor, C. J., & Clegg, P. (2017) · Journal of Orthopaedic Research® · doi:10.1002/jor.23563 · Cited by 10
de Assis, D. R., Szabó, A., Osete, J. R., Puppo, F., O’Connell, K. S., Akkouh, I. A., Hughes, T., Frei, E., Andreassen, O. A., & Djurovic, S. (2021) · Cells · doi:10.3390/cells10020209 · Cited by 10
Kunze, M., & Malfatti, F. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.70071 · Cited by 9
Wang, X., Bajpai, A. K., Gu, Q., Ashbrook, D. G., Starlard‐Davenport, A., & Lu, L. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1009462 · Cited by 9
Francis, E. C., Kechris, K. J., Johnson, R. K., Rawal, S., Pathmasiri, W., Rushing, B. R., Du, X., Jansson, T., Dabelea, D., Sumner, S., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25147620 · Cited by 9
A Systems Approach to Drug Discovery in Alzheimer's Disease
Shelanski, M. L., Shin, W., Aubry, S., Sims, P. A., Alvarez, M. J., & Califano, A. (2015) · Neurotherapeutics · doi:10.1007/s13311-014-0335-5 · Cited by 9
Metadata
- Authors
- Miller, J. A., Horvath, S., & Geschwind, D. H.
- Affiliations
- University of California, Los Angeles (US)
- Departments of cHuman Genetics and
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2010-06-25
- Volume
- 107
- Issue
- 28
- Pages
- 12698-12703
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2906579
- OpenAlex
- W2139511828
- Cited by
- 480
- References
- 52
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 27 · 2023: 20 · 2022: 35 · 2021: 31 · 2020: 34 · 2019: 47 · 2018: 35 · 2017: 39 · 2016: 35 · 2015: 36 · 2014: 26 · 2013: 35 · 2012: 33
- FWCI
- 11.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (HL028481)
- National Institute on Aging (F31AG031649, U01AG016976, R01 AG26938-05, R01AG026938)
- National Heart, Lung, and Blood Institute (P01HL028481)
- National Institute of Mental Health (R37MH060233, R37 MH 60233-09S1)
- National Institute of Allergy and Infectious Diseases (U19AI063603)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, gene co-expression network, cell type markers, glial cells, microglia, oligodendrocytes, presenilin 1, network hubs
- MeSH terms
- Alzheimer Disease, Animals, Brain, Humans, Male, Gene Expression Profiling, Systems Biology, Mice, Gene Regulatory Networks, Presenilin-1
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review