The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics
Abstract
Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation.
Overview
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
Figures



![Figure 4 Two validated transcriptional regulatory interactions exhibiting high time-lagged correlations. (A) Rel and Nfkb1. The solid line shows the expression of Rel (c-REL), and the dotted line shows the expression of Nfkb1 (p50/p105) in LPS-stimulated wild-type macrophages, over eight hours. The genes exhibit a high time-lagged correlation with a time delay of 60 minutes (across the eleven time-course experiments listed in Table S9, ρτ = 0.91 and P = 0.011; see Materials and Methods, Time-lagged Correlation, for an explanation of the statistical test). The NFκB heterodimers c-REL-p50 and c-REL-p65 are known to regulate expression of Nfkb1 [43]. The correlation at zero time lag is 0.81. (B) Irf7 and Stat1. The solid line shows the expression of Irf7 (IRF7) and the dotted line shows the expression of Stat1 (STAT1) in LPS-stimulated Atf3 (−/−) macrophages. The genes exhibit a high time-lagged correlation with a time delay of 20 minutes (across the ten experiments, ρτ = 0.96 and P = 0.002). The transcription factor IRF7 has been shown to regulate the Stat1 gene expression in the innate immune response to viral infection [44]. The correlation at zero time lag is 0.95. (C) Time-lagged correlation coefficient and time-lagged correlation significance measure (see Equation 4) as a function of the time lag τ, for Irf7 and Stat1. The peak value of ρτ 2 occurs at τ = 10, but the peak significance value (taking into account the lag-specific null distribution) occurs at τ = 20 min.](/paper-figures/e5ce1586b9f147d285c680888a1c5db6/uncovering-a-macrophage-transcriptional-program-by-integrating-evidence-from-figure-4.jpg)


Verification
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?
In the citing paper
“For macrophages stimulated with TPP, the TF network also included STAT4 , as well as TFs associated with negative regulation of TLR signaling ( HEY1 ; Hu et al., 2008), macrophage activation ( TGIF1 ; Ramsey et al., 2008), or TFs at the interface between inflammation and metabolism ( HIF1A ; Shay and Celeste Simon, 2012).”
From Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation (2014) · cites this paper as Ramsey et al., 2008
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
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
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Takeda, K., Kaisho, T., & Akira, S. (2003) · Annual Review of Immunology · doi:10.1146/annurev.immunol.21.120601.141126 · Cited by 6,143
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
Measuring Information Transfer
Schreiber, T. (2000) · Physical Review Letters · doi:10.1103/physrevlett.85.461 · Cited by 4,698
Role of Adaptor TRIF in the MyD88-Independent Toll-Like Receptor Signaling Pathway
Yamamoto, M., Sato, S., Hemmi, H., Hoshino, K., Kaisho, T., Sanjo, H., Takeuchi, O., Sugiyama, M., Okabe, M., Takeda, K., et al. (2003) · Science · doi:10.1126/science.1087262 · Cited by 3,295
Targeted disruption of the MyD88 gene results in loss of IL-1- and IL-18-mediated function.
Adachi, O., Kawai, T., Takeda, K., Matsumoto, M., Tsutsui, H., Sakagami, M., Nakanishi, K., & Akira, S. (1998) · Immunity · doi:10.1016/s1074-7613(00)80596-8 · Cited by 2,358
Transcriptional regulatory code of a eukaryotic genome
Harbison, C. T., Gordon, D. B., Lee, T. I., Rinaldi, N. J., MacIsaac, K. D., Danford, T., Hannett, N. M., Tagne, J., Reynolds, D. B., Yoo, J. Y., et al. (2004) · Nature · doi:10.1038/nature02800 · Cited by 2,135
Lipocalin 2 mediates an innate immune response to bacterial infection by sequestrating iron
Flo, T. H., Smith, K. D., Sato, S., Rodriguez, D. J., Holmes, M. A., Strong, R. K., Akira, S., & Aderem, A. (2004) · Nature · doi:10.1038/nature03104 · Cited by 1,877
Self-citationHonda, K., & Taniguchi, T. (2006) · Nature reviews. Immunology · doi:10.1038/nri1900 · Cited by 1,722
Reverse engineering of regulatory networks in human B cells
Basso, K., Margolin, A. A., Stolovitzky, G. A., Klein, U., Dalla‐Favera, R., & Califano, A. (2005) · Nature Genetics · doi:10.1038/ng1532 · Cited by 1,418
Identification of Lps2 as a key transducer of MyD88-independent TIR signalling
Hoebe, K. H. N., Du, X., Georgel, P., Janssen, E. M., Tabeta, K., Kim, S. O., Goode, J., Lin, P., Mann, N., Mudd, S., et al. (2003) · Nature · doi:10.1038/nature01889 · Cited by 1,232
Hayden, M. S., West, A. P., & Ghosh, S. (2006) · Oncogene · doi:10.1038/sj.onc.1209943 · Cited by 1,178
Peri, S., Navarro, J. D., Amanchy, R., Kristiansen, T. Z., Jonnalagadda, C. K., Surendranath, V., Niranjan, V., Muthusamy, B., Gandhi, T. K. B., Grønborg, M., et al. (2003) · Genome Research · doi:10.1101/gr.1680803 · Cited by 1,090
TRANSFAC: a database on transcription factors and their DNA binding sites
Wingender, E. (1996) · Nucleic Acids Research · doi:10.1093/nar/24.1.238 · Cited by 1,059
Probing Gene Expression in Live Cells, One Protein Molecule at a Time
Yu, J., Xiao, J., Ren, X., Lao, K., & Xie, X. S. (2006) · Science · doi:10.1126/science.1119623 · Cited by 918
Shakhov, A. N., Collart, M. A., Vassalli, P., Nedospasov, S. A., & Jongeneel, C. V. (1990) · The Journal of Experimental Medicine · doi:10.1084/jem.171.1.35 · Cited by 819
Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4
Gilchrist, M., Thorsson, V., Li, B., Rust, A. G., Korb, M., Kennedy, K., Hai, T., Bolouri, H., & Aderem, A. (2006) · Nature · doi:10.1038/nature04768 · Cited by 803
Self-citationArrayExpress--a public database of microarray experiments and gene expression profiles
Parkinson, H., Kapushesky, M., Shojatalab, M., Abeygunawardena, N., Coulson, R., Farne, A., Holloway, E., Kolesnykov, N., Lilja, P., Lukk, M., et al. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl995 · Cited by 754
Oscillatory Expression of Hes1, p53, and NF-κB Driven by Transcriptional Time Delays
Monk, N. A. M. (2003) · Current Biology · doi:10.1016/s0960-9822(03)00494-9 · Cited by 708
Toll-like receptors: key mediators of microbe detection
Underhill, D. M., & Ozinsky, A. (2002) · Current Opinion in Immunology · doi:10.1016/s0952-7915(01)00304-1 · Cited by 685
Insights into host responses against pathogens from transcriptional profiling
Jenner, R. G., & Young, R. A. (2005) · Nature Reviews Microbiology · doi:10.1038/nrmicro1126 · Cited by 588
Zou, M., & Conzen, S. D. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bth463 · Cited by 587 · cited 1×
“Several techniques have been developed specifically for model inference from time-course expression data, notably dynamic Bayesian networks (DBN) [21] and ODE-based model selection [12].”
MethodThe Biomolecular Interaction Network Database and related tools 2005 update
Alfarano, C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki051 · Cited by 587
The E2F family: specific functions and overlapping interests
Attwooll, C., Denchi, E. L., & Helin, K. (2004) · The EMBO Journal · doi:10.1038/sj.emboj.7600481 · Cited by 573
Human macrophage activation programs induced by bacterial pathogens
Nau, G. J., Richmond, J. F. L., Schlesinger, A., Jennings, E. G., Lander, E. S., & Young, R. A. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.022649799 · Cited by 568
Significance analysis of time course microarray experiments
Storey, J. D., Xiao, W., Leek, J. T., Tompkins, R. G., & Davis, R. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504609102 · Cited by 567
Severe impairment of permatogenesis in mice lacking the CREM gene
Blendy, J. A., Kaestner, K. H., Weinbauer, G. F., Nieschlag, E., & Schütz, G. (1996) · Nature · doi:10.1038/380162a0 · Cited by 520
Bonneau, R., Reiss, D. J., Shannon, P., Facciotti, M., Hood, L., Baliga, N. S., & Thorsson, V. (2006) · Genome Biology · doi:10.1186/gb-2006-7-5-r36 · Cited by 510
Detection of functional DNA motifs via statistical over-representation
Frith, M. C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh299 · Cited by 424
A gene encoding a protein with zinc fingers is activated during G0/G1 transition in cultured cells.
Chavrier, P., Zerial, M., Lemaire, P., Almendral, J. M., Bravo, R., & Charnay, P. (1988) · The EMBO Journal · doi:10.1002/j.1460-2075.1988.tb02780.x · Cited by 408
Transcriptional regulation in the immune system: all roads lead to AP-1
Foletta, V. C., Segal, D., & Cohén, D. (1998) · Journal of Leukocyte Biology · doi:10.1002/jlb.63.2.139 · Cited by 393
GM-CSF in inflammation and autoimmunity
Hamilton, J. A. (2002) · Trends in Immunology · doi:10.1016/s1471-4906(02)02260-3 · Cited by 379
Altered Patterns of Gene Expression in Response to Myocardial Infarction
Stanton, L. W., Garrard, L. J., Damm, D. L., Garrick, B. L., Lam, A., Kapoun, A. M., Zheng, Q., Protter, A. A., Schreiner, G. F., & White, R. T. (2000) · Circulation Research · doi:10.1161/01.res.86.9.939 · Cited by 367
A data integration methodology for systems biology
Hwang, D., Rust, A. G., Ramsey, S. A., Smith, J. J., Pedrioli, D. M. L., Weston, A. D., de Atauri, P., Aitchison, J. D., Hood, L., Siegel, A. F., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0508647102 · Cited by 332
Self-citationInferring gene regulatory networks from multiple microarray datasets
Wang, Y., Joshi, T., Zhang, X., Xu, D. H., & Chen, L. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl396 · Cited by 324
Role for Activating Transcription Factor 3 in Stress-Induced β-Cell Apoptosis
Hartman, M. G., Lu, D., Kim, M.-L., Kociba, G. J., Shukri, T., Buteau, J., Wang, X., Frankel, W. L., Guttridge, D., Prentki, M., et al. (2004) · Molecular and Cellular Biology · doi:10.1128/mcb.24.13.5721-5732.2004 · Cited by 313
Bertolino, E., Reimund, B., Wildt‐Perinic, D., & Clerc, R. G. (1995) · Journal of Biological Chemistry · doi:10.1074/jbc.270.52.31178 · Cited by 307
Elkon, R., Linhart, C., Sharan, R., Shamir, R., & Shiloh, Y. (2003) · Genome Research · doi:10.1101/gr.947203 · Cited by 300
A test case of correlation metric construction of a reaction pathway from measurements
Arkin, A., Shen, P., & Ross, J. (1997) · Science · doi:10.1126/science.277.5330.1275 · Cited by 291
STAT1 as a key modulator of cell death
Kim, H. S., & Lee, M. (2006) · Cellular Signalling · doi:10.1016/j.cellsig.2006.09.003 · Cited by 284
The UCSC genome browser database: update 2007
Kuhn, R. M., Karolchik, D., Zweig, A. S., Trumbower, H., Thomas, D. J., Thakkapallayil, A., Sugnet, C. W., Stanke, M., Smith, K. E., Siepel, A. C., et al. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl928 · Cited by 279
Oscillatory Regulation of Hes1: Discrete Stochastic Delay Modelling and Simulation
Barrio-Solórzano, M., Burrage, K., Leier, A., & Tian, T. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020117 · Cited by 270
Nuclear receptors in macrophage biology: at the crossroads of lipid metabolism and inflammation
Castrillo, A., & Tontonoz, P. (2004) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.20.012103.134432 · Cited by 260
Global and Distinct Targets of IRF-5 and IRF-7 during Innate Response to Viral Infection
Barnes, B. J., Richards, J. O., Mancl, M. E., Hanash, S., Beretta, L., & Pitha, P. M. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m400726200 · Cited by 221
The Elements of Statistical Learning
Ziegel, E. R. (2003) · Technometrics · doi:10.1198/tech.2003.s770 · Cited by 199
Rahman, A., Anwar, K. N., True, A. L., & Malik, A. B. (1999) · The Journal of Immunology · doi:10.4049/jimmunol.162.9.5466 · Cited by 172
Rahman, A., Anwar, K. N., True, A. L., & Malik, A. B. (1999) · PubMed · Cited by 163
Zhao, W., Serpedin, E., & Dougherty, E. R. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl364 · Cited by 155
Statistical construction of chemical reaction mechanisms from measured time-series
Arkin, A., & Ross, J. (1995) · The Journal of Physical Chemistry · doi:10.1021/j100003a020 · Cited by 143
Mukherjee, B., Morgenbesser, S. D., & DePinho, R. A. (1992) · Genes & Development · doi:10.1101/gad.6.8.1480 · Cited by 126
TGIF Inhibits Retinoid Signaling
Bartholin, L., Powers, S. E., Melhuish, T. A., Lasse, S., Weinstein, M. B., & Wotton, D. (2006) · Molecular and Cellular Biology · doi:10.1128/mcb.26.3.990-1001.2006 · Cited by 125
Transcriptional network dynamics in macrophage activation
Nilsson, R., Bajić, V. B., Suzuki, H., di Bernardo, D., Björkegren, J., Katayama, S., Reid, J. F., Sweet, M. J., Gariboldi, M., Carninci, P., et al. (2006) · Genomics · doi:10.1016/j.ygeno.2006.03.022 · Cited by 124
Haverty, P. M. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh183 · Cited by 112
Promoter of the human NF-kappa B p50/p105 gene. Regulation by NF-kappa B subunits and by c-REL
Cogswell, P. C., Scheinman, R. I., & Baldwin, A. S. (1993) · The Journal of Immunology · doi:10.4049/jimmunol.150.7.2794 · Cited by 102
Pessah, M., Prunier, C., Marais, J., Ferrand, N., Mazars, A., Lallemand, F., Gauthier, J., & Atfi, A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.101579798 · Cited by 100
Schmitt, W. A., Raab, R. M., & Stephanopoulos, G. N. (2004) · Genome Research · doi:10.1101/gr.2439804 · Cited by 99
Chiang, D. Y., Brown, P. O., & Eisen, M. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s49 · Cited by 99
Splicing Precedes Polyadenylation during Drosophila E74A Transcription
Lemaire, M. F., & Thummel, C. S. (1990) · Molecular and Cellular Biology · doi:10.1128/mcb.10.11.6059-6063.1990 · Cited by 97
Goldmann, O., von Köckritz‐Blickwede, M., Höltje, C., Chhatwal, G. S., Geffers, R., & Medina, E. (2007) · Infection and Immunity · doi:10.1128/iai.00181-07 · Cited by 94
Hu, H.-M., Tian, Q., Baer, M., Spooner, C. J., Williams, S. C., Johnson, P. F., & Schwartz, R. C. (2000) · Journal of Biological Chemistry · doi:10.1074/jbc.m910269199 · Cited by 93
Barthel, R., Tsytsykova, A. V., Barczak, A. K., Tsai, E. Y., Dascher, C. C., Brenner, M. B., & Goldfeld, A. E. (2003) · Molecular and Cellular Biology · doi:10.1128/mcb.23.2.526-533.2003 · Cited by 90
INCLUSive: INtegrated Clustering, Upstream sequence retrieval and motif Sampling
Thijs, G., Moreau, Y., Smet, F. D., Mathys, J., Lescot, M., Rombauts, S., Rouzé, P., Moor, B. D., & Kathleen Marchal, D. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.2.331 · Cited by 85
Liu, J., Guan, X., & Ma, X. (2005) · Journal of Biological Chemistry · doi:10.1074/jbc.m500973200 · Cited by 82
Marziali, G., Perrotti, E., Ilari, R., Coccia, E. M., Mantovani, R., Testa, U., & Battistini, A. (1999) · Blood · doi:10.1182/blood.v93.2.519 · Cited by 81
The Innate Immune Response to Infection
Kaufmann, S. H. E., Medzhitov, R. M., & Gordon, S. (2004) · ASM Press eBooks · doi:10.1128/9781555817671 · Cited by 76
Jak-STAT signaling pathways in cells of the immune system.
Ivashkiv, L. B. (2000) · PubMed · Cited by 65
Nolan, K. F., Strong, V., Soler, D. F., Fairchild, P. J., Cobbold, S. P., Croxton, R. S., Gonzalo, J.-Á., Rubio, A., Wells, M. P., & Waldmann, H. (2004) · The Journal of Immunology · doi:10.4049/jimmunol.172.4.2201 · Cited by 64
Roach, J. C., Smith, K. D., Strobe, K. L., Nissen, S. M., Haudenschild, C. D., Zhou, D., Vasicek, T. J., Held, G. A., Stolovitzky, G. A., Hood, L., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707757104 · Cited by 64
Self-citationComputational reconstruction of transcriptional regulatory modules of the yeast cell cycle
Wu, W.-S., Li, W., & Chen, B. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-421 · Cited by 61
Identifying gene regulatory networks from experimental data
Chen, T., Filkov, V., & Skiena, S. (2001) · Parallel Computing · doi:10.1016/s0167-8191(00)00092-2 · Cited by 58
Integrating genomic data to predict transcription factor binding.
Holloway, D. T., Kon, M., & DeLisi, C. (2005) · PubMed · Cited by 55
Topology of mammalian transcription networks.
Potapov, A. P., Voss, N., Sasse, N., & Wingender, E. (2005) · PubMed · Cited by 54
Mostecki, J. W., Showalter, B. M., & Rothman, P. B. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m408938200 · Cited by 48
Carey, B. C., Staudt, M. K., Bonaminio, D. N., van der Loo, J. C. M., & Trapnell, B. C. (2007) · The Journal of Immunology · doi:10.4049/jimmunol.178.4.2440 · Cited by 37
Smad6 is a protein kinase X phosphorylation substrate and is required for HL-60 cell differentiation
Glesne, D. A., & Huberman, E. (2006) · Oncogene · doi:10.1038/sj.onc.1209436 · Cited by 32
Chastel, C., Jiricny, J., & Jaussi, R. (2004) · DNA repair · doi:10.1016/j.dnarep.2003.12.002 · Cited by 30
Lag Analysis of Genetic Networks in the Cell Cycle of Budding Yeast
Kato, M., Tsunoda, T., & Takagi, T. (2001) · doi:10.11234/gi1990.12.266 · Cited by 29
Sasmono, R. T., & Hume, D. (2004) · Cited by 29
Hirano, S., Anuradha, C. D., & Kanno, S. (2000) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/ajrcmb.23.3.4112 · Cited by 27
Feifei, C. H. E. N., Ogawa, K., Xubao, L. I. U., Stringfield, T. M., & Chen, Y. (2002) · Biochemical Journal · doi:10.1042/bj20011703 · Cited by 17
SARGE: a tool for creation of putative genetic networks
Shaw, O., Harwood, C. R., Steggles, L. J., & Wipat, A. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth395 · Cited by 15
A Dynamic Time-Lagged Correlation Based Method To Learn Multi-Time Delay Gene Networks
Agrawal, A., & Mittal, A. (2007) · doi:10.5281/zenodo.1072166 · Cited by 8
Sasmono, R. T., & Hume, D. A. (2014) · ASM Press eBooks · doi:10.1128/9781555817671.ch4 · Cited by 7
Incorporating genome-scale tools for studying energy homeostasis
Raab, R. M. (2006) · Nutrition & Metabolism · doi:10.1186/1743-7075-3-40 · Cited by 5
Neelankavil, J. P. (2015) · doi:10.4324/9781315689876-33 · Cited by 5
A Dynamic Time-Lagged Correlation based Method to Learn Multi-Time Delay Gene
Agrawal, A., & Mittal, A. (2005) · Cited by 3
Cited by
Inflammatory links between obesity and metabolic disease
Lumeng, C. N., & Saltiel, A. R. (2011) · Journal of Clinical Investigation · doi:10.1172/jci57132 · Cited by 2,319
Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation
Xue, J., Schmidt, S. V., Sander, J., Draffehn, A., Krebs, W., Quester, I., De Nardo, D., Gohel, T. D., Emde, M., Schmidleithner, L., et al. (2014) · Immunity · doi:10.1016/j.immuni.2014.01.006 · Cited by 2,210
Rabani, M., Levin, J. Z., Fan, L., Adiconis, X., Raychowdhury, R., Garber, M., Gnirke, A., Nusbaum, C., Hacohen, N., Friedman, N., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1861 · Cited by 583
Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses
Amit, I., Garber, M., Chevrier, N., Leite, A. P., Donner, Y., Eisenhaure, T., Guttman, M., Grenier, J. K., Li, W., Zuk, O., et al. (2009) · Science · doi:10.1126/science.1179050 · Cited by 488
The FANTOM Consortium, & Riken Omics Science Center (2009) · Nature Genetics · doi:10.1038/ng.375 · Cited by 427
Metadata
- Authors
- Ramsey, S. A., Klemm, S. L., Zak, D. E., Kennedy, K. A., Thorsson, V., Li, B., Gilchrist, M., Gold, E. S., Johnson, C. D., Litvak, V., Navarro, G., Roach, J. C., Rosenberger, C. M., Rust, A. G., Yudkovsky, N., Aderem, A., & Shmulevich, I.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-03-21
- Volume
- 4
- Issue
- 3
- Pages
- e1000021
- ISSN
- 1553-7358
- PMC
- PMC2265556
- Cited by
- 147
- References
- 92
- Language
- en
- Update notices
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.