Causal analysis approaches in Ingenuity Pathway Analysis
Open accessAbstract
MOTIVATION: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. RESULTS: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. AVAILABILITY: The causal analytics tools 'Upstream Regulator Analysis', 'Mechanistic Networks', 'Causal Network Analysis' and 'Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
IPA identified BMP9 (or GDF2), MyD88, and mTOR as upstream small molecules to target the Hif1a regulon and XO4+ network.
“IPA identified BMP9 (or GDF2), a ligand of the TGF-β superfamily, the Toll-like receptor (TLR) adaptor MyD88, and mTOR”
From Transcriptional signature in microglia associated with Aβ plaque phagocytosis (2021) · cites this paper as [55]
The Introduction and §3.3 describe IPA as a method for predicting upstream regulators from gene-expression data. But the paper’s examples in §5.1 and §5.3 do not report BMP9/GDF2, MyD88, or mTOR as regulators of a Hif1a regulon or XO4+ network. The source supports use of IPA for this kind of analysis, not the specific findings attributed to it. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 20
- Abatangelo L Comparative study of gene set enrichment methods BMC Bioinform. 2009 10 275 PMC2746222 19725948
- Arteaga MF The histone demethylase PHF8 governs retinoic acid response in acute promyelocytic leukemia Cancer cell 2013 3 376 389 23518351 PMC6812572
- Chindelevitch L Causal reasoning on biological networks: interpreting transcriptional changes Bioinformatics 2012a 28 1114 1121 22355083
- Chindelevitch L Assessing statistical significance in causal graphs BMC Bioinform. 2012b 13 35 PMC3307026 22348444
- Felciano RM Predictive systems biology approach to broad-spectrum, host-directed drug target discovery in infectious diseases Pac. Symp. Biocomput. 2013 2013 17 28 23424108
- Go JT 2009 pandemic H1N1 influenza virus elicits similar clinical course but differential host transcriptional response in mouse, macaque, and swine infection models BMC Genom. 2012 13 627 PMC3532173 23153050
- Hayden A S-adenosylhomocysteine hydrolase inhibition by 3-deazaneplanocin A analogues induces anti-cancer effects in breast cancer cell lines and synergy with both histone deacetylase and HER2 inhibition Breast Cancer Res. Treat. 2011 1 109 119 20556507
- Haynes KR Excessive bone formation in a mouse model of ankylosing spondylitis is associated with decreases in Wnt pathway inhibitors Arth. Res. Ther. 2012 14 R253 23171658 PMC3674607
- Kumar R Causal reasoning identifies mechanisms of sensitivity for a novel AKT kinase inhibitor, GSK690693 BMC Genom. 2010 11 419 PMC2996947 20604938
- Lamb J The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease Science 2006 313 1929 1935 17008526
- Lin CY Whole-genome cartography of estrogen receptor alpha binding sites PLoS Genet. 2007 3 e87 17542648 PMC1885282
- Martin F Assessment of network perturbation amplitudes by applying high-throughput data to causal networks BMC Syst. Biol. 2012 6 54 22651900 PMC3433335
- Meier-Abt F Parity induces differentiation and reduces Wnt/Notch signaling ratio and proliferation potential of basal stem/progenitor cells isolated from mouse mammary epithelium Breast Cancer Res. 2013 2 R36 23621987 PMC3672662
- Muller P The anti-estrogenic effect of all-trans-retinoic acid on the breast cancer cell line MCF-7 is dependent on HES-1 expression J.Biol. Chem. 2002 32 28376 28379 12080040
- Ombra MN Retinoic acid impairs estrogen signaling in breast cancer cells by interfering with activation of LSD1 via PKA Biochimica et Biophysica Acta 2013 1829 480 486 23507259
- Pollard J Jr. A computational model to define the molecular causes of type 2 diabetes mellitus Diabetes Technol. Ther. 2005 7 323 336 15857235
- Shneiderman B Tree visualization with tree-maps: 2-d space-filling approach ACM Transact. Graph. 1992 11 92 99
- Viemann D TNF induces distinct gene expression programs in microvascular and macrovascular human endothelial cells J. Leukoc Biol. 2006 80 174 185 16617158
- Wang Q 1,25-Dihydroxyvitamin D3 and all-trans-retinoic acid sensitize breast cancer cells to chemotherapy-induced cell death Cancer Res. 2000 7 2040 2048 10766196
- Zhu WY Retinoic acid inhibition of cell cycle progression in MCF-7 human breast cancer cells Exp. Cell Res. 1997 234 293 299 9260897
Cited by
Sorted by citations
NicheNet: Modeling intercellular communication by linking ligands to target genes
Browaeys, R., Saelens, W., & Saeys, Y. (2020) · Nature Methods · doi:10.1038/s41592-019-0667-5 · Cited by 2,192 · see citing works
Proteomic and Metabolomic Characterization of COVID-19 Patient Sera
Shen, B., Yi, X., Sun, Y., Bi, X., Du, J., Zhang, C., Quan, S., Zhang, F., Sun, R., Qian, L., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.032 · Cited by 1,514 · see citing works
Roulois, D., Loo Yau, H., Singhania, R., Wang, Y., Danesh, A., Shen, S. Y., Han, H., Liang, G., Jones, P. A., Pugh, T. J., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.056 · Cited by 1,527 · see citing works
Deciphering cell–cell interactions and communication from gene expression
Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00292-x · Cited by 1,267 · see citing works
Li, H., Courtois, E. T., Sengupta, D., Tan, Y., Chen, K. H., Goh, J. J. L., Kong, S. L., Chua, C., Hon, L. K., Tan, W. S., et al. (2017) · Nature Genetics · doi:10.1038/ng.3818 · Cited by 1,080 · see citing works
Alvarez, M. J., Shen, Y., Giorgi, F. M., Lachmann, A., Ding, B. B., Ye, B. H., & Califano, A. (2016) · Nature Genetics · doi:10.1038/ng.3593 · Cited by 966 · see citing works
Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19
Silvin, A., Chapuis, N., Dunsmore, G., Goubet, A.-G., Dubuisson, A., Derosa, L., Almire, C., Hénon, C., Kosmider, O., Droin, N., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.002 · Cited by 834 · see citing works
Microbiome Influences Prenatal and Adult Microglia in a Sex-Specific Manner
Thion, M. S., Low, D., Silvin, A., Chen, J., Grisel, P., Schulte-Schrepping, J., Blecher, R., Ulas, T., Squarzoni, P., Hoeffel, G., et al. (2018) · Cell · doi:10.1016/j.cell.2017.11.042 · Cited by 779 · see citing works
WNT/β-catenin pathway activation correlates with immune exclusion across human cancers
Luke, J. J., Bao, R., Sweis, R. F., Spranger, S., & Gajewski, T. F. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-1942 · Cited by 722 · see citing works
Systems biology and multi-omics integration: viewpoints from the metabolomics research community
Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723 · see citing works
An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637 · see citing works
Integrated omics: tools, advances and future approaches
Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A. (2019) · Journal of Molecular Endocrinology · doi:10.1530/jme-18-0055 · Cited by 686 · see citing works
Immune evasion mediated by PD-L1 on glioblastoma-derived extracellular vesicles
Ricklefs, F. L., Alayo, Q., Krenzlin, H., Mahmoud, A. B., Speranza, M. C., Nakashima, H., Hayes, J. L., Lee, K., Balaj, L., Passaro, C., et al. (2018) · Science Advances · doi:10.1126/sciadv.aar2766 · Cited by 610 · see citing works
Tau protein aggregation is associated with cellular senescence in the brain
Musi, N., Valentine, J. M., Sickora, K. R., Baeuerle, E., Thompson, C. S., Shen, Q., & Orr, M. E. (2018) · Aging Cell · doi:10.1111/acel.12840 · Cited by 595 · see citing works
Longitudinal multi-omics of host–microbe dynamics in prediabetes
Zhou, W., Sailani, M. R., Contrepois, K., Zhou, Y., Ahadi, S., Leopold, S. R., Zhang, M. J., Rao, V., Avina, M., Mishra, T., et al. (2019) · Nature · doi:10.1038/s41586-019-1236-x · Cited by 583 · see citing works
Airway microbiota is associated with upregulation of the PI3K pathway in lung cancer
Tsay, J.-C. J., Wu, B. G., Badri, M. H., Clemente, J. C., Shen, N., Meyn, P., Li, Y., Yie, T.-A., Lhakhang, T., Olsen, E., et al. (2018) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201710-2118oc · Cited by 448 · see citing works
Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration
Moiseeva, V., Cisneros, A., Sica, V., Deryagin, O., Lai, Y., Jung, S., Andrés, E., An, J., Segalés, J., Ortet, L., et al. (2023) · Nature · doi:10.1038/s41586-022-05535-x · Cited by 423 · see citing works
Cell-cell communication: New insights and clinical implications
Su, J., Song, Y., Zhu, Z., Huang, X., Fan, J., Qiao, J., & Mao, F. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01888-z · Cited by 433 · see citing works
Cholesterol and matrisome pathways dysregulated in astrocytes and microglia
TCW, J., Qian, L., Pipalia, N. H., Chao, M. J., Liang, S. A., Shi, Y., Jain, B. R., Bertelsen, S. E., Kapoor, M., Marcora, E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.017 · Cited by 411 · see citing works
Witcher, K. G., Bray, C. E., Chunchai, T., Zhao, F., O'Neil, S. M., Gordillo, A. J., Campbell, W. A., McKim, D. B., Liu, X., Dziabis, J. E., et al. (2021) · The Journal of Neuroscience · doi:10.1523/jneurosci.2469-20.2020 · Cited by 387 · see citing works
Impaired Mitochondrial Biogenesis in Adipose Tissue in Acquired Obesity
Heinonen, S., Buzkova, J., Muniandy, M., Kaksonen, R., Ollikainen, M., Ismail, K., Hakkarainen, A., Lundbom, J., Lundbom, N., Vuolteenaho, K., et al. (2015) · Diabetes · doi:10.2337/db14-1937 · Cited by 362 · see citing works
Silvin, A., Uderhardt, S., Piot, C., Da Mesquita, S., Yang, K., Geirsdottir, L., Mulder, K., Eyal, D., Liu, Z., Bridlance, C., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.07.004 · Cited by 368 · see citing works
Trisomy 21 consistently activates the interferon response
Sullivan, K. D., Lewis, H. C., Hill, A. A., Pandey, A., Jackson, L. P., Cabral, J. M., Smith, K. P., Liggett, L. A., Gomez, E. B., Galbraith, M. D., et al. (2016) · eLife · doi:10.7554/elife.16220 · Cited by 362 · see citing works
Transcriptional signature in microglia associated with Aβ plaque phagocytosis
Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23111-1 · Cited by 357 · see citing works
Microglial NF-κB drives tau spreading and toxicity in a mouse model of tauopathy
Wang, C., Fan, L., Khawaja, R. R., Liu, B., Zhan, L., Kodama, L., Chin, M., Li, Y., Le, D., Zhou, Y., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29552-6 · Cited by 333 · see citing works
Interleukin-1β Induces mtDNA Release to Activate Innate Immune Signaling via cGAS-STING
Aarreberg, L. D., Esser-Nobis, K., Driscoll, C., Shuvarikov, A., Roby, J. A., & Gale, M. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.02.038 · Cited by 328 · see citing works
Lower Airway Dysbiosis Affects Lung Cancer Progression
Tsay, J.-C. J., Wu, B. G., Sulaiman, I., Gershner, K., Schluger, R., Li, Y., Yie, T.-A., Meyn, P., Olsen, E., Perez, L., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0263 · Cited by 322 · see citing works
Schiller, H. B., Fernandez, I. E., Burgstaller, G., Schaab, C., Scheltema, R. A., Schwarzmayr, T., Strom, T. M., Eickelberg, O., & Mann, M. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156123 · Cited by 261 · see citing works
Duan, Q., McMahon, S., Anand, P., Shah, H., Thomas, S., Salunga, H. T., Huang, Y., Zhang, R., Sahadevan, A., Lemieux, M. E., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aah5084 · Cited by 262 · see citing works
Wang, X., He, Q., Zhou, C., Xu, Y., Liu, D., Fujiwara, N., Kubota, N., Click, A., Henderson, P., Vancil, J., et al. (2023) · Immunity · doi:10.1016/j.immuni.2022.11.013 · Cited by 262 · see citing works
Proteomic differences in amyloid plaques in rapidly progressive and sporadic Alzheimer’s disease
Drummond, E., Nayak, S., Faustin, A., Pires, G., Hickman, R. A., Askenazi, M., Cohen, M., Haldiman, T., Kim, C., Han, X., et al. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1691-0 · Cited by 239 · see citing works
Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk
Walker, K. A., Chen, J., Zhang, J., Fornage, M., Yang, Y., Zhou, L., Grams, M. E., Tin, A., Daya, N., Hoogeveen, R. C., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00064-0 · Cited by 212 · see citing works
van der Lugt, B., van Beek, A. A., Aalvink, S., Meijer, B., Sovran, B., Vermeij, W. P., Brandt, R. M. C., de Vos, W. M., Savelkoul, H. F. J., Steegenga, W. T., et al. (2019) · Immunity & Ageing · doi:10.1186/s12979-019-0145-z · Cited by 217 · see citing works
Sexually divergent induction of microglial-associated neuroinflammation with hippocampal aging
Mangold, C. A., Wronowski, B., Du, M., Masser, D. R., Hadad, N., Bixler, G. V., Brucklacher, R. M., Ford, M. M., Sonntag, W. E., & Freeman, W. M. (2017) · Journal of Neuroinflammation · doi:10.1186/s12974-017-0920-8 · Cited by 203 · see citing works
Plemel, J. R., Stratton, J. A., Michaels, N. J., Rawji, K. S., Zhang, E., Sinha, S., Baaklini, C. S., Dong, Y., Ho, M., Thorburn, K., et al. (2020) · Science Advances · doi:10.1126/sciadv.aay6324 · Cited by 208 · see citing works
Banerjee, S., Tian, T., Wei, Z., Shih, N., Feldman, M. D., Alwine, J. C., Coukos, G., & Robertson, E. S. (2017) · Oncotarget · doi:10.18632/oncotarget.16717 · Cited by 204 · see citing works
Descalzi, G., Mitsi, V., Purushothaman, I., Gaspari, S., Avrampou, K., Loh, Y.-H. E., Shen, L., & Zachariou, V. (2017) · Science Signaling · doi:10.1126/scisignal.aaj1549 · Cited by 188 · see citing works
Co-evolution of Matrisome and Adaptive Adhesion Dynamics Drives Ovarian Cancer Chemoresistance
Pietilä, E. A., Gonzalez-Molina, J., Moyano-Galceran, L., Jamalzadeh, S., Zhang, K., Lehtinen, L., Turunen, S. P., Martins, T. A., Gultekin, O., Lamminen, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24009-8 · Cited by 175 · see citing works
Bhattacherjee, A., Djekidel, M. N., Chen, R., Chen, W., Tuesta, L. M., & Zhang, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-12054-3 · Cited by 174 · see citing works
Bai, Z., Woodhouse, S., Zhao, Z., Arya, R., Govek, K., Kim, D., Lundh, S., Baysoy, A., Sun, H., Deng, Y., et al. (2022) · Science Advances · doi:10.1126/sciadv.abj2820 · Cited by 166 · see citing works
The acinar differentiation determinant PTF1A inhibits initiation of pancreatic ductal adenocarcinoma
Krah, N. M., De La O, J.-P., Swift, G. H., Hoang, C. Q., Willet, S. G., Chen Pan, F., Cash, G. M., Bronner, M. P., Wright, C. V., MacDonald, R. J., et al. (2015) · eLife · doi:10.7554/elife.07125 · Cited by 166 · see citing works
Kiss, T., Nyúl-Tóth, Á., Balasubramanian, P., Tarantini, S., Ahire, C., Yabluchanskiy, A., Csipo, T., Farkas, E., Wren, J. D., Garman, L., et al. (2020) · GeroScience · doi:10.1007/s11357-020-00165-5 · Cited by 163 · see citing works
Walker, K. A., Chen, J., Shi, L., Yang, Y., Fornage, M., Zhou, L., Schlosser, P., Surapaneni, A., Grams, M. E., Duggan, M. R., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adf5681 · Cited by 148 · see citing works
Precardiac organoids form two heart fields via Bmp/Wnt signaling
Andersen, P., Tampakakis, E., Jimenez, D. V., Kannan, S., Miyamoto, M., Shin, H. K., Saberi, A., Murphy, S., Sulistio, E., Chelko, S. P., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05604-8 · Cited by 132 · see citing works
Fajgenbaum, D. C., Langan, R.-A., Japp, A. S., Partridge, H. L., Pierson, S. K., Singh, A., Arenas, D. J., Ruth, J. R., Nabel, C. S., Stone, K., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci126091 · Cited by 147 · see citing works
Preoperative stimulation of resolution and inflammation blockade eradicates micrometastases
Panigrahy, D., Gartung, A., Yang, J., Yang, H., Gilligan, M. M., Sulciner, M. L., Bhasin, S. S., Bielenberg, D. R., Chang, J., Schmidt, B. A., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci127282 · Cited by 132 · see citing works
DNA methylation networks underlying mammalian traits
Haghani, A., Li, C. Z., Robeck, T. R., Zhang, J., Lu, A. T., Ablaeva, J., Acosta-Rodríguez, V. A., Adams, D. M., Alagaili, A. N., Almunia, J., et al. (2023) · Science · doi:10.1126/science.abq5693 · Cited by 128 · see citing works
Osipovitch, M., Asenjo Martinez, A., Mariani, J. N., Cornwell, A., Dhaliwal, S., Zou, L., Chandler-Militello, D., Wang, S., Li, X., Benraiss, S.-J., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2018.11.010 · Cited by 120 · see citing works
Briggs, C. E., Wang, Y., Kong, B., Woo, T.-U. W., Iyer, L. K., & Sonntag, K. C. (2015) · Brain Research · doi:10.1016/j.brainres.2015.05.021 · Cited by 113 · see citing works
Taylor, S., Ominsky, M. S., Hu, R., Pacheco, E., He, Y. D., Brown, D. L., Aguirre, J. I., Wronski, T. J., Buntich, S., Afshari, C. A., et al. (2016) · Bone · doi:10.1016/j.bone.2015.12.013 · Cited by 106 · see citing works
Bouquet, J., Soloski, M. J., Swei, A., Cheadle, C., Federman, S., Billaud, J.-N., Rebman, A. W., Kabre, B., Halpert, R., Boorgula, M., et al. (2016) · mBio · doi:10.1128/mbio.00100-16 · Cited by 101 · see citing works
Daskalakis, N. P., Cohen, H., Nievergelt, C. M., Baker, D. G., Buxbaum, J. D., Russo, S. J., & Yehuda, R. (2016) · Experimental Neurology · doi:10.1016/j.expneurol.2016.07.024 · Cited by 105 · see citing works
Lee, T.-Y., Huang, K.-Y., Chuang, C.-H., Lee, C.-Y., & Chang, T.-H. (2020) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2020.107277 · Cited by 95 · see citing works
Lauterstein, D., Tijerina, P., Corbett, K., Akgol Oksuz, B., Shen, S., Gordon, T., Klein, C., & Zelikoff, J. (2016) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph13040417 · Cited by 99 · see citing works
Beheshti, A., Chakravarty, K., Fogle, H., Fazelinia, H., Silveira, W. A. D., Boyko, V., Polo, S.-H. L., Saravia-Butler, A. M., Hardiman, G., Taylor, D., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-55869-2 · Cited by 90 · see citing works
Vitamin C–squalene bioconjugate promotes epidermal thickening and collagen production in human skin
Gref, R., Deloménie, C., Maksimenko, A., Gouadon, E., Percoco, G., Lati, E., Desmaële, D., Zouhiri, F., & Couvreur, P. (2020) · Scientific Reports · doi:10.1038/s41598-020-72704-1 · Cited by 97 · see citing works
Kim, D. K., Park, J., Han, D., Yang, J., Kim, A., Woo, J., Kim, Y., & Mook-Jung, I. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0234-4 · Cited by 95 · see citing works
Guo, Y., Jardin, B. D., Zhou, P., Sethi, I., Akerberg, B. N., Toepfer, C. N., Ai, Y., Li, Y., Ma, Q., Guatimosim, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06347-2 · Cited by 96 · see citing works
Jiang, S., Maphis, N. M., Binder, J., Chisholm, D., Weston, L., Duran, W., Peterson, C., Zimmerman, A., Mandell, M. A., Jett, S. D., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109720 · Cited by 92 · see citing works
Wang, H., Li, Y., Ryder, J. W., Hole, J. T., Ebert, P. J., Airey, D. C., Qian, H.-R., Logsdon, B., Fisher, A., Ahmed, Z., et al. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0296-y · Cited by 83 · see citing works
Shaashua, L., Ben-Shmuel, A., Pevsner-Fischer, M., Friedman, G., Levi-Galibov, O., Nandakumar, S., Barki, D., Nevo, R., Brown, L. E., Zhang, W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34081-3 · Cited by 79 · see citing works
Genomics implicates adaptive and innate immunity in Alzheimer’s and Parkinson’s diseases
Gagliano, S. A., Pouget, J. G., Hardy, J., Knight, J., Barnes, M. R., Ryten, M., & Weale, M. E. (2016) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.369 · Cited by 85 · see citing works
Gao, Y., Ray, S., Dai, S., Ivanov, A. R., Abu‐Absi, N. R., Lewis, A. M., Huang, Z., Xing, Z., Borys, M. C., Li, Z. J., et al. (2016) · Biotechnology Journal · doi:10.1002/biot.201600030 · Cited by 76 · see citing works
Sterenborg, R. B. T. M., Steinbrenner, I., Li, Y., Bujnis, M. N., Naito, T., Marouli, E., Galesloot, T. E., Babajide, O., Andreasen, L., Astrup, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44701-9 · Cited by 79 · see citing works
Saggau, C., Bacher, P., Esser, D., Rasa, M., Meise, S., Mohr, N., Kohlstedt, N., Hutloff, A., Schacht, S.-S., Dargvainiene, J., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.08.005 · Cited by 79 · see citing works
Baumann, D., Hägele, T., Mochayedi, J., Drebant, J., Vent, C., Blobner, S., Noll, J. H., Nickel, I., Schumacher, C., Boos, S. L., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15979-2 · Cited by 76 · see citing works
van der Lugt, B., Rusli, F., Lute, C., Lamprakis, A., Salazar, E., Boekschoten, M. V., Hooiveld, G. J., Müller, M., Vervoort, J., Kersten, S., et al. (2018) · Aging · doi:10.18632/aging.101439 · Cited by 72 · see citing works
Bhattacherjee, A., Zhang, C., Watson, B. R., Djekidel, M. N., Moffitt, J. R., & Zhang, Y. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01455-9 · Cited by 71 · see citing works
Dedifferentiation and neuronal repression define familial Alzheimer’s disease
Caldwell, A. B., Liu, Q., Schroth, G. P., Galasko, D. R., Yuan, S. H., Wagner, S. L., & Subramaniam, S. (2020) · Science Advances · doi:10.1126/sciadv.aba5933 · Cited by 69 · see citing works
Shi, L., Rocha, M., Zhang, W., Jiang, M., Li, S., Ye, Q., Hassan, S. H., Liu, L., Adair, M. N., Xu, J., et al. (2020) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x20925655 · Cited by 71 · see citing works
Distinguishing benign from pathologic TH2 immunity in atopic children
Holt, P. G., Strickland, D., Bosco, A., Belgrave, D., Hales, B., Simpson, A., Hollams, E., Holt, B., Kusel, M., Ahlstedt, S., et al. (2016) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2015.08.044 · Cited by 68 · see citing works
Deconstructing Stepwise Fate Conversion of Human Fibroblasts to Neurons by MicroRNAs
Cates, K., McCoy, M. J., Kwon, J.-S., Liu, Y., Abernathy, D. G., Zhang, B., Liu, S., Gontarz, P., Kim, W. K., Chen, S., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2020.08.015 · Cited by 67 · see citing works
Transcriptome analysis of airborne PM 2.5-induced detrimental effects on human keratinocytes
Kim, H.-J., Bae, I.-H., Son, E. D., Park, J., Cha, N., Na, H.-W., Jung, C., Go, Y.-S., Kim, D.-Y., Lee, T. R., et al. (2017) · Toxicology Letters · doi:10.1016/j.toxlet.2017.03.010 · Cited by 58 · see citing works
Deng, F.-L., Pan, J.-X., Zheng, P., Xia, J.-J., Yin, B.-M., Liang, W.-W., Li, Y.-F., Wu, J., Xu, F., Wu, Q.-Y., et al. (2019) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s186071 · Cited by 58 · see citing works
Renfrow, J. J., Soike, M. H., West, J. L., Ramkissoon, S. H., Metheny-Barlow, L., Mott, R. T., Kittel, C. A., D’Agostino, R. B., Tatter, S. B., Laxton, A. W., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-72290-2 · Cited by 45 · see citing works
Ozaki, Y., Saito, K., Nakazawa, K., Konishi, M., Itoh, N., Hakuno, F., Takahashi, S.-I., Kato, H., & Takenaka, A. (2015) · British Journal Of Nutrition · doi:10.1017/s0007114515002846 · Cited by 51 · see citing works
Lee, C. A. A., Seo, H. S., Armien, A. G., Bates, F. S., Tolar, J., & Azarin, S. M. (2018) · Fluids and Barriers of the CNS · doi:10.1186/s12987-018-0094-5 · Cited by 52 · see citing works
Podleśny-Drabiniok, A., Novikova, G., Liu, Y., Dunst, J., Temizer, R., Giannarelli, C., Marro, S., Kreslavsky, T., Marcora, E., & Goate, A. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46315-7 · Cited by 52 · see citing works
The microbiota restrains neurodegenerative microglia in a model of amyotrophic lateral sclerosis
Cox, L. M., Calcagno, N., Gauthier, C., Madore, C., Butovsky, O., & Weiner, H. L. (2022) · Microbiome · doi:10.1186/s40168-022-01232-z · Cited by 53 · see citing works
Reininger, D., Fundel‐Clemens, K., Mayr, C. H., Wollin, L., Laemmle, B., Quast, K., Nickolaus, P., & Herrmann, F. E. (2024) · British Journal of Pharmacology · doi:10.1111/bph.17303 · Cited by 48 · see citing works
“(d) IPA upstream regulator analysis integrating the rat bleomycin model with nerandomilast treatment and human IPF data (pre-selected genes of the pulmonary fibrosis idiopathic signalling pathway ). bleo, bleomycin; IPA, Ingenuity Pathway Analysis; IPF, idiopathic pulmonary fibrosis; logFC, log-fold change.”
InfluentialBarber, A. J., del Genio, C. L., Swain, A. B., Pizzi, E. M., Watson, S. C., Tapiavala, V. N., Zanazzi, G. J., & Gaur, A. B. (2024) · Alzheimer's Research & Therapy · doi:10.1186/s13195-024-01492-x · Cited by 41 · see citing works
Desjardins, P., Berthiaume, R., Couture, C., Le-Bel, G., Roy, V., Gros-Louis, F., Moulin, V. J., Proulx, S., Chemtob, S., Germain, L., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232012201 · Cited by 39 · see citing works
Mazo, C. E., D’Lugos, A. C., Sweeney, K. R., Haus, J. M., Angadi, S. S., Carroll, C. C., & Dickinson, J. M. (2021) · European Journal of Applied Physiology · doi:10.1007/s00421-021-04758-6 · Cited by 36 · see citing works
Machine learning integration of multimodal data identifies key features of blood pressure regulation
Louca, P., Tran, T. Q. B., Toit, C. D., Christofidou, P., Spector, T. D., Mangino, M., Suhre, K., Padmanabhan, S., & Menni, C. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104243 · Cited by 37 · see citing works
Histone deacetylases 1 and 2 restrain CD4+ cytotoxic T lymphocyte differentiation
Preglej, T., Hamminger, P., Luu, M., Bulat, T., Andersen, L., Göschl, L., Stolz, V., Rica, R., Sandner, L., Waltenberger, D., et al. (2020) · JCI Insight · doi:10.1172/jci.insight.133393 · Cited by 36 · see citing works
Industrial Big Data Analytics: Challenges and Opportunities
Al-Abassi, A., Karimipour, H., HaddadPajouh, H., Dehghantanha, A., & Parizi, R. M. (2020) · Handbook of Big Data Privacy · doi:10.1007/978-3-030-38557-6_3 · Cited by 32 · see citing works
Toor, A., Culibrk, L., Singhera, G. K., Moon, K.-M., Prudova, A., Foster, L. J., Moore, M. M., Dorscheid, D. R., & Tebbutt, S. J. (2018) · PLOS ONE · doi:10.1371/journal.pone.0209652 · Cited by 32 · see citing works
Anti-angiogenic effects of mangiferin and mechanism of action in metastatic melanoma
Delgado-Hernández, R., Hernández-Balmaseda, I., Rodeiro-Guerra, I., Cesar Rodriguez Gonzalez, J., De Wever, O., Logie, E., Declerck, K., Pérez-Novo, C., & Vanden Berghe, W. (2020) · Melanoma Research · doi:10.1097/cmr.0000000000000647 · Cited by 29 · see citing works
Kang, M., Ang, T. F. A., Devine, S. A., Sherva, R., Mukherjee, S., Trittschuh, E. H., Gibbons, L. E., Scollard, P., Lee, M., Choi, S.-E., et al. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00633-4 · Cited by 29 · see citing works
Maybury‐Lewis, S. Y., Brown, A. K., Yeary, M., Sloutskin, A., Dhakal, S., Juven‐Gershon, T., & Webb, A. E. (2021) · Aging Cell · doi:10.1111/acel.13499 · Cited by 27 · see citing works
Noel, B. M., Ouellette, S. B., Marholz, L., Dickey, D., Navis, C., Yang, T.-Y., Nguyen, V., Parker, S. J., Bernlohr, D., Sachs, Z., et al. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00028 · Cited by 24 · see citing works
Logan, N. J., Camman, M., Williams, G., & Higgins, C. A. (2018) · Bone · doi:10.1016/j.bone.2018.09.008 · Cited by 21 · see citing works
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis
Liu, T., Chen, T., Zheng, W., Luo, X., Chen, Y., & Zhao, H. (2023) · doi:10.1101/2023.12.07.569910 · Cited by 30 · see citing works
Gene expression profiles underlying aggressive behavior in the prefrontal cortex of cattle
Eusebi, P. G., Sevane, N., O’Rourke, T., Pizarro, M., Boeckx, C., & Dunner, S. (2021) · BMC Genomics · doi:10.1186/s12864-021-07505-5 · Cited by 24 · see citing works
Kolbe, E., Aleithe, S., Rennert, C., Spormann, L., Ott, F., Meierhofer, D., Gajowski, R., Stöpel, C., Hoehme, S., Kücken, M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.104 · Cited by 22 · see citing works
Wiley, J. W., Higgins, G. A., & Hong, S. (2022) · PeerJ · doi:10.7717/peerj.13287 · Cited by 21 · see citing works
Haga, M., Iida, K., & Okada, M. (2024) · iScience · doi:10.1016/j.isci.2024.109708 · Cited by 19 · see citing works
The small CRL4CSA ubiquitin ligase component DDA1 regulates transcription-coupled repair dynamics
Llerena Schiffmacher, D. A., Lee, S.-H., Kliza, K. W., Theil, A. F., Akita, M., Helfricht, A., Bezstarosti, K., Gonzalo-Hansen, C., van Attikum, H., Verlaan-de Vries, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50584-7 · Cited by 19 · see citing works
Dong, Y.-W., Blanchard, T. S., Noll, A., Vasquez, P., Schmitz, J., Kelly, S. P., Wright, P. A., & Whitehead, A. (2020) · Journal of Experimental Biology · doi:10.1242/jeb.235515 · Cited by 18 · see citing works
Impact of aging on meningeal gene expression
Neutzner, M., Kohler, C., Frank, S., Killer, H. E., & Neutzner, A. (2023) · Fluids and Barriers of the CNS · doi:10.1186/s12987-023-00412-9 · Cited by 17 · see citing works
- Authors
- Krämer, A., Green, J., Pollard, J., & Tugendreich, S.
- Affiliations
- Sanofi (France) (FR)
- Corresponding author
- Krämer, Andreas
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-12-13
- Volume
- 30
- Issue
- 4
- Pages
- 523-530
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3928520
- OpenAlex
- W2162143298
- Cited by
- 5,724
- References
- 0
- Citations by year
- 2026: 402 · 2025: 601 · 2024: 695 · 2023: 745 · 2022: 670 · 2021: 725 · 2020: 584 · 2019: 449 · 2018: 335 · 2017: 210 · 2016: 157 · 2015: 107 · 2014: 39 · 2013: 1
- FWCI
- 45.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Sanofi
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- causal network inference, gene expression data interpretation, Ingenuity Pathway Analysis, mechanistic hypotheses, disease prediction, regulatory networks
- MeSH terms
- Algorithms, Breast Neoplasms, Female, Humans, Causality, Computational Biology, Gene Expression Profiling, Knowledge Bases, Gene Regulatory Networks, Human Umbilical Vein Endothelial Cells, MCF-7 Cells
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
