Reverse engineering of regulatory networks in human B cells

Emergence of Scaling in Random Networks

Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135

Scale-Free Networks

Barabási, A.-L., & Bonabeau, E. (2003) · Scientific American · doi:10.1038/scientificamerican0503-60 · Cited by 2,494

Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling

Gardner, T. S., di Bernardo, D., Lorenz, D., & Collins, J. J. (2003) · Science · doi:10.1126/science.1081900 · Cited by 1,117

ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-s1-s7 · Cited by 2,420

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

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles

Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J. J., & Gardner, T. S. (2007) · PLoS Biology · doi:10.1371/journal.pbio.0050008 · Cited by 1,563

The c-Myc target gene network

Dang, C. V., O’Donnell, K. A., Zeller, K. I., Nguyen, T., Osthus, R. C., & Li, F. (2006) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2006.07.014 · Cited by 1,188

Functional characterization of somatic mutations in cancer using network-based inference of protein activity

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

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792

Gene regulatory network inference: Data integration in dynamic models—A review

Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755

Predictive Behavior Within Microbial Genetic Networks

Tagkopoulos, I., Liu, Y.-C., & Tavazoie, S. (2008) · Science · doi:10.1126/science.1154456 · Cited by 519

Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight

Ghazalpour, A., Doss, S., Zhang, B., Wang, S., Plaisier, C., Castellanos, R., Brozell, A., Schadt, E. E., Drake, T. A., Lusis, A. J., et al. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020130 · Cited by 462

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

Franke, L., Bakel, H. V., Fokkens, L., de Jong, E. D., Egmont-Petersen, M., & Wijmenga, C. (2006) · The American Journal of Human Genetics · doi:10.1086/504300 · Cited by 476

Circuitry and Dynamics of Human Transcription Factor Regulatory Networks

Neph, S., Stergachis, A. B., Reynolds, A., Sandstrom, R., Borenstein, E., & Stamatoyannopoulos, J. A. (2012) · Cell · doi:10.1016/j.cell.2012.04.040 · Cited by 467

Comparison of co-expression measures: mutual information, correlation, and model based indices

Song, L., Langfelder, P., & Horvath, S. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-328 · Cited by 460

Towards a rigorous assessment of systems biology models: the DREAM3 challenges

Prill, R. J., Marbach, D., Saez-Rodriguez, J., Sorger, P. K., Alexopoulos, L. G., Xue, X., Clarke, N. D., Altan-Bonnet, G., & Stolovitzky, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009202 · Cited by 439

The H3K27me3 demethylase JMJD3 contributes to the activation of the INK4A–ARF locus in response to oncogene- and stress-induced senescence

Agger, K., Cloos, P. A. C., Rudkjær, L., Williams, K., Andersen, G., Christensen, J., & Helin, K. (2009) · Genes & Development · doi:10.1101/gad.510809 · Cited by 427

Reverse engineering cellular networks

Margolin, A. A., Wang, K., Lim, W. K., Kustagi, M., Nemenman, I., & Califano, A. (2006) · Nature Protocols · doi:10.1038/nprot.2006.106 · Cited by 379

AID-Dependent Activation of a MYC Transgene Induces Multiple Myeloma in a Conditional Mouse Model of Post-Germinal Center Malignancies

Chesi, M., Robbiani, D. F., Sebag, M., Chng, W. J., Affer, M., Tiedemann, R., Valdez, R., Palmer, S. E., Haas, S. S., Stewart, A. K., et al. (2008) · Cancer Cell · doi:10.1016/j.ccr.2008.01.007 · Cited by 393

Protein kinase C and cancer: what we know and what we do not

Garg, R., Benedetti, L. G., Abera, M. B., Wang, H., Abba, M., & Kazanietz, M. G. (2014) · Oncogene · doi:10.1038/onc.2013.524 · Cited by 303

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E. (2014) · Genes, Brain and Behavior · doi:10.1111/gbb.12106 · Cited by 289

Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

Emmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00038 · Cited by 282

DREAM4: combining genetic and dynamic information to identify biological networks and dynamical models

Greenfield, A., Madar, A., Ostrer, H., & Bonneau, R. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013397 · Cited by 255

TimeDelay-ARACNE: reverse engineering of gene networks from time-course data by an information theoretic approach

Zoppoli, P., Morganella, S., & Ceccarelli, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-154 · Cited by 248

The c‐myc Promoter: Still MysterY and Challenge

(2008) · Advances in Cancer Research · doi:10.1016/s0065-230x(07)99004-1 · Cited by 211

A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors

Alvarez, M. J., Subramaniam, P. S., Tang, L. H., Grunn, A., Aburi, M., Rieckhof, G., Komissarova, E. V., Hagan, E. A., Bodei, L., Clemons, P. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0138-4 · Cited by 210

Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks

Chen, J. C., Alvarez, M. J., Talos, F., Dhruv, H., Rieckhof, G. E., Iyer, A., Diefes, K. L., Aldape, K., Berens, M., Shen, M. M., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.021 · Cited by 188

Comparing Statistical Methods for Constructing Large Scale Gene Networks

Allen, J. D., Xie, Y., Chen, M., Girard, L., & Xiao, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029348 · Cited by 186

Review on statistical methods for gene network reconstruction using expression data

Wang, Y. X. R., & Huang, H. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.03.040 · Cited by 182

Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states

Chandrasekaran, S., Ament, S. A., Eddy, J. A., Rodriguez-Zas, S. L., Schatz, B. R., Price, N. D., & Robinson, G. E. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114093108 · Cited by 176

A CRISPR/Cas9-Engineered ARID1A -Deficient Human Gastric Cancer Organoid Model Reveals Essential and Nonessential Modes of Oncogenic Transformation

Lo, Y.-H., Kolahi, K. S., Du, Y., Chang, C.-Y., Krokhotin, A., Nair, A., Sobba, W. D., Karlsson, K., Jones, S. J., Longacre, T. A., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1109 · Cited by 174

Integrative proteogenomic characterization of hepatocellular carcinoma across etiologies and stages

Ng, C. K. Y., Dazert, E., Boldanova, T., Coto-Llerena, M., Nuciforo, S., Ercan, C., Suslov, A., Meier, M.-A., Bock, T., Schmidt, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29960-8 · Cited by 165

Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks

Rendeiro, A. F., Schmidl, C., Strefford, J. C., Walewska, R., Davis, Z., Farlik, M., Oscier, D., & Bock, C. (2016) · Nature Communications · doi:10.1038/ncomms11938 · Cited by 146

Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics

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., et al. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000021 · Cited by 147

Modulatory profiling identifies mechanisms of small molecule-induced cell death

Wolpaw, A. J., Shimada, K., Skouta, R., Welsch, M. E., Akavia, U. D., Pe'er, D., Shaik, F., Bulinski, J. C., & Stockwell, B. R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1106149108 · Cited by 133

Pharmacological inhibition of the transcription factor PU.1 in leukemia

Antony-Debré, I., Paul, A., Leite, J., Mitchell, K., Kim, H. M., Carvajal, L. A., Todorova, T. I., Huang, K., Kumar, A., Farahat, A. A., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci92504 · Cited by 120

Targeting cap-dependent translation blocks converging survival signals by AKT and PIM kinases in lymphoma

Schatz, J. H., Oricchio, E., Wolfe, A. L., Jiang, M., Linkov, I., Maragulia, J., Shi, W., Zhang, Z., Rajasekhar, V. K., Pagano, N. C., et al. (2011) · Journal of Experimental Medicine · doi:10.1084/jem.20110846 · Cited by 119

Comparative study of RNA-seq- and Microarray-derived coexpression networks in Arabidopsis thaliana

Giorgi, F. M., Del Fabbro, C., & Licausi, F. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt053 · Cited by 103

Assembly and Interrogation of Alzheimer’s Disease Genetic Networks Reveal Novel Regulators of Progression

Aubry, S., Shin, W., Crary, J. F., Lefort, R., Qureshi, Y. H., Lefebvre, C., Califano, A., & Shelanski, M. L. (2015) · PLOS ONE · doi:10.1371/journal.pone.0120352 · Cited by 106

Efficient reverse-engineering of a developmental gene regulatory network

Crombach, A., Wotton, K. R., Cicin-Sain, D., Ashyraliyev, M., & Jaeger, J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002589 · Cited by 83

A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide

Wren, J. D. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp290 · Cited by 95

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models

Erguler, K., & Stumpf, M. P. H. (2011) · Molecular BioSystems · doi:10.1039/c0mb00107d · Cited by 91

Regulatory network control of blood stem cells

Göttgens, B. (2015) · Blood · doi:10.1182/blood-2014-08-570226 · Cited by 82

A Systems Biology Approach Identifies Molecular Networks Defining Skeletal Muscle Abnormalities in Chronic Obstructive Pulmonary Disease

Turan, N., Kalko, S., Stincone, A., Clarke, K., Sabah, A., Howlett, K., Curnow, S. J., Rodriguez, D. A., Cascante, M., O'Neill, L., et al. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002129 · Cited by 74

Systematic elucidation and pharmacological targeting of tumor-infiltrating regulatory T cell master regulators

Obradovic, A., Ager, C., Turunen, M., Nirschl, T., Khosravi-Maharlooei, M., Iuga, A., Jackson, C. M., Yegnasubramanian, S., Tomassoni, L., Fernandez, E. C., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.04.003 · Cited by 66

Microglia recapitulate a hematopoietic master regulator network in the aging human frontal cortex

Wehrspaun, C. C., Haerty, W., & Ponting, C. P. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.04.008 · Cited by 64

Identifying candidate drivers of alcohol dependence-induced excessive drinking by assembly and interrogation of brain-specific regulatory networks

Repunte-Canonigo, V., Shin, W., Vendruscolo, L. F., Lefebvre, C., van der Stap, L., Kawamura, T., Schlosburg, J. E., Alvarez, M., Koob, G. F., Califano, A., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0593-5 · Cited by 52

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

Mundi, P. S., Dela Cruz, F. S., Grunn, A., Diolaiti, D., Mauguen, A., Rainey, A. R., Guillan, K. C., Siddiquee, A., You, D., Realubit, R., et al. (2021) · doi:10.1101/2021.10.03.462951 · Cited by 51

Employing core regulatory circuits to define cell identity

Almeida, N., Chung, M. W. H., Drudi, E. M., Engquist, E. N., Hamrud, E., Isaacson, A., Tsang, V. S. K., Watt, F. M., & Spagnoli, F. M. (2021) · The EMBO Journal · doi:10.15252/embj.2020106785 · Cited by 49

Computational Biology and Drug Discovery: From Single-Target to Network Drugs

Ambesi-Impiombato, A., & Bernardo, D. (2006) · Current Bioinformatics · doi:10.2174/157489306775330598 · Cited by 47

Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms

Kang, Y., Thieffry, D., & Cantini, L. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.617282 · Cited by 41

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction

Chandrashekar, P. B., Alatkar, S., Wang, J., Hoffman, G. E., He, C., Jin, T., Khullar, S., Bendl, J., Fullard, J. F., Roussos, P., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01248-6 · Cited by 37

The center for causal discovery of biomedical knowledge from big data

Cooper, G. F., Bahar, I., Becich, M. J., Benos, P. V., Berg, J., Espino, J., Glymour, C., Jacobson, R. C., Kienholz, M., Lee, A. V., et al. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv059 · Cited by 36

Reverse engineering and analysis of large genome-scale gene networks

Aluru, M., Zola, J., Nettleton, D., & Aluru, S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks904 · Cited by 34

Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model

Wang, Y., Yang, S., Zhao, J., Du, W., Liang, Y., Wang, C., Zhou, F., Tian, Y., & Ma, Q. (2019) · Scientific Reports · doi:10.1038/s41598-019-40780-7 · Cited by 26

Robust network inference using response logic

Gross, T., Wongchenko, M. J., Yan, Y., & Blüthgen, N. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz326 · Cited by 7