Passing Messages between Biological Networks to Refine Predicted Interactions
Open accessAbstract
Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.
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A focus on TFs is rarely sufficient, and integration of multiple data types is often required to increase the accuracy and usefulness of the resulting networks.
“However, a focus on TFs is rarely sufficient, and integration of multiple data types is often required to increase the accuracy and usefulness of the resulting networks [13, 147].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [13]
Integration of multiple data types increases the accuracy of the resulting predictions.
“Overall, integration of multiple data types increases the accuracy of the resulting predictions [13, 147].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [13]
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Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890 · see citing works
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,733 · see citing works
Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes
Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., & Brown, P. O. (2000) · Molecular Biology of the Cell · doi:10.1091/mbc.11.12.4241 · Cited by 4,815 · see citing works
BioGRID: a general repository for interaction datasets
Stark, C. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj109 · Cited by 3,960 · see citing works
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 · see citing works
Margolin, A. A., Nemenman, I. M., Basso, K., Wiggins, C. H., Stolovitzky, G. A., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.7916/d8g15zbp · Cited by 2,260 · see citing works
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 2,116 · see citing works
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., & Friedman, N. (2003) · Nature Genetics · doi:10.1038/ng1165 · Cited by 1,686 · see citing works
Wisdom of crowds for robust gene network inference
The DREAM5 Consortium, Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2016 · Cited by 1,667 · see citing works
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 · see citing works
Architecture of the human regulatory network derived from ENCODE data
Gerstein, M., Kundaje, A. B., Hariharan, M., Landt, S. G., Yan, K., Cheng, C., Mu, X. J., Khurana, E., Rozowsky, J. S., Alexander, R. P., et al. (2012) · Nature · doi:10.1038/nature11245 · Cited by 1,418 · see citing works
Transcription regulation and animal diversity
Levine, M., & Tjian, R. (2003) · Nature · doi:10.1038/nature01763 · Cited by 1,309 · see citing works
The BioGRID interaction database: 2013 update
Chatr‐aryamontri, A., Breitkreutz, B., Heinicke, S., Boucher, L., Winter, A. G., Stark, C., Nixon, J., Ramage, L., Kolas, N. K., O’Donnell, L., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1158 · Cited by 1,010 · see citing works
Butte, A. J., & Kohane, I. S. (1999) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814447331_0040 · Cited by 956 · see citing works
Genetic network inference: from co-expression clustering to reverse engineering
D’haeseleer, P., Liang, S., & Somogyi, R. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.8.707 · Cited by 943 · see citing works
A Computer Program for Classifying Plants
Rogers, D. J., & Tanimoto, T. T. (1960) · Science · doi:10.1126/science.132.3434.1115 · Cited by 874 · see citing works
The BioGRID Interaction Database: 2011 update
Stark, C., Breitkreutz, B., Chatr‐aryamontri, A., Boucher, L., Oughtred, R., Livstone, M. S., Nixon, J., Auken, K. M. V., Wang, X., Shi, X., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1116 · Cited by 853 · see citing works
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 · see citing works
Revealing strengths and weaknesses of methods for gene network inference
Marbach, D., Prill, R. J., Schaffter, T., Mattiussi, C., Floreano, D., & Stolovitzky, G. A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913357107 · Cited by 755 · see citing works
Computational discovery of gene modules and regulatory networks
Bar‐Joseph, Z., Gerber, G. K., Lee, T. I., Rinaldi, N. J., Yoo, J. Y., Robert, F., Gordon, D. B., Fraenkel, E., Jaakkola, T., Young, R. A., et al. (2003) · Nature Biotechnology · doi:10.1038/nbt890 · Cited by 606 · see citing works
Discovery of meaningful associations in genomic data using partial correlation coefficients
de la Fuente, A., Bing, N., Hoeschele, I., & Mendes, P. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth445 · Cited by 552 · see citing works
Zhu, J., Zhang, B., Smith, E. N., Drees, B., Brem, R. B., Kruglyak, L., Bumgarner, R. E., & Schadt, E. E. (2008) · Nature Genetics · doi:10.1038/ng.167 · Cited by 549 · see citing works
Pique-Regi, R., Degner, J. F., Pai, A. A., Gaffney, D. J., Gilad, Y., & Pritchard, J. K. (2011) · Genome Research · doi:10.1101/gr.112623.110 · Cited by 521 · see citing works
Advantages and limitations of current network inference methods
Smet, R. D., & Marchal, K. (2010) · Nature Reviews Microbiology · doi:10.1038/nrmicro2419 · Cited by 510 · see citing works
Schmitt, A. P., & McEntee, K. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.12.5777 · Cited by 472 · see citing works
A Role for the Transcription Factors Mbp1 and Swi4 in Progression from G1 to S Phase
Koch, C., Moll, T., Neuberg, M., Ahorn, H., & Nasmyth, K. (1993) · Science · doi:10.1126/science.8372350 · Cited by 382 · see citing works
Integrating regulatory motif discovery and genome-wide expression analysis
Conlon, E. M., Liu, X. S., Lieb, J. D., & Liu, J. S. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0630591100 · Cited by 343 · see citing works
Chuang, H., Hofree, M., & Ideker, T. (2010) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100109-104122 · Cited by 316 · see citing works
Tata, P., Wu, W., Miles, S., Noble, W. S., & Breeden, L. L. (2006) · Genes & Development · doi:10.1101/gad.1450606 · Cited by 281 · see citing works
Cell cycle regulted transcription in yeast
Koch, C., & Nasmyth, K. (1994) · Current Opinion in Cell Biology · doi:10.1016/0955-0674(94)90039-6 · Cited by 273 · see citing works
COMBINING LOCATION AND EXPRESSION DATA FOR PRINCIPLED DISCOVERY OF GENETIC REGULATORY NETWORK MODELS
Hartemink, A. J., Gifford, D. K., Jaakkola, T., & Young, R. A. (2001) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812799623_0041 · Cited by 267 · see citing works
Zhu, J., Wiener, M. C., Zhang, C., Fridman, A., Minch, E., Lum, P. Y., Sachs, J. R., & Schadt, E. E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030069 · Cited by 209 · see citing works
Identifying transcription factor functions and targets by phenotypic activation
Chua, G., Morris, Q., Sopko, R., Robinson, M. D., Ryan, O. W., Chan, E. T., Frey, B. J., Andrews, B., Boone, C. M., & Hughes, T. R. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605140103 · Cited by 183 · see citing works
Tata, P., Miles, S., GuhaThakurta, D., Jemiolo, D., & Breeden, L. L. (2002) · Genes & Development · doi:10.1101/gad.1034302 · Cited by 170 · see citing works
Yeang, C., Ideker, T. E., & Jaakkola, T. (2004) · Journal of Computational Biology · doi:10.1089/1066527041410382 · Cited by 167 · see citing works
Reverse engineering gene regulatory networks
Hartemink, A. J. (2005) · Nature Biotechnology · doi:10.1038/nbt0505-554 · Cited by 144 · see citing works
de Bruin, R. A. M., Kalashnikova, T. I., Chahwan, C., McDonald, W. H., Wohlschlegel, J. A., Yates, J. R., Russell, P. R., & Wittenberg, C. (2006) · Molecular Cell · doi:10.1016/j.molcel.2006.06.025 · Cited by 143 · see citing works
Learning by Message Passing in Networks of Discrete Synapses
Braunstein, A., & Zecchina, R. (2006) · Physical Review Letters · doi:10.1103/physrevlett.96.030201 · Cited by 142 · see citing works
Guthke, R., Moller, U., Hoffmann, M., Thiès, F., & Topfer, S. (2004) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti226 · Cited by 129 · see citing works
Marbach, D., Roy, S., Ay, F., Meyer, P. E., Candeias, R., Kahveci, T., Bristow, C. A., & Kellis, M. (2012) · Genome Research · doi:10.1101/gr.127191.111 · Cited by 122 · see citing works
Tunçbağ, N., Braunstein, A., Pagnani, A., Huang, S. C., Chayes, J., Borgs, C., Zecchina, R., & Fraenkel, E. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2012.0092 · Cited by 114 · see citing works
Revealing differences in gene network inference algorithms on the network level by ensemble methods
Altay, G., & Emmert‐Streib, F. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq259 · Cited by 110 · see citing works
A Semi-Supervised Method for Predicting Transcription Factor–Gene Interactions in Escherichia coli
Ernst, J., Beg, Q. K., Kay, K. A., Balázsi, G., Oltvai, Z. N., & Bar‐Joseph, Z. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000044 · Cited by 103 · see citing works
BNFinder: exact and efficient method for learning Bayesian networks
Wilczyński, B., & Dojer, N. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn505 · Cited by 100 · see citing works
Inferring transcriptional modules from ChIP-chip, motif and microarray data
Lemmens, K., Dhollander, T., Bie, T. D., Monsieurs, P., Engelen, K., Smets, B., Winderickx, J., Moor, B. D., & Kathleen Marchal, D. (2006) · Genome biology · doi:10.1186/gb-2006-7-5-r37 · Cited by 94 · see citing works
Integrated Assessment and Prediction of Transcription Factor Binding
Beyer, A., Workman, C. T., Hollunder, J., Radke, D., Möller, U., Wilhelm, T. E., & Ideker, T. E. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020070 · Cited by 91 · see citing works
Distinct roles for Khd1p in the localization and expression of bud-localized mRNAs in yeast
Hasegawa, Y., Irie, K., & Gerber, A. P. (2008) · RNA · doi:10.1261/rna.1016508 · Cited by 77 · see citing works
Selective integration of multiple biological data for supervised network inference
Kato, T., Tsuda, K., & Asai, K. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti339 · Cited by 72 · see citing works
Siegmund, R. F., & Nasmyth, K. (1996) · Molecular and Cellular Biology · doi:10.1128/mcb.16.6.2647 · Cited by 66 · see citing works
Ness, F., Bourot, S., Régnacq, M., Spagnoli, R., Bergès, T., & Karst, F. (2001) · European Journal of Biochemistry · doi:10.1046/j.1432-1327.2001.02029.x · Cited by 66 · see citing works
Bonnet, É., Tatari, M. N., Joshi, A., Michoel, T., Kathleen Marchal, D., Berx, G., & de Peer, Y. V. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010162 · Cited by 62 · see citing works
Mihalik, Á., & Csermely, P. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002187 · Cited by 61 · see citing works
Michoel, T., Smet, R. D., Joshi, A., de Peer, Y. V., & Kathleen Marchal, D. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-49 · Cited by 60 · see citing works
Structural influence of gene networks on their inference: analysis of C3NET
Altay, G., & Emmert‐Streib, F. (2011) · Biology Direct · doi:10.1186/1745-6150-6-31 · Cited by 57 · see citing works
Modeling Gene Regulation Networks Using Ordinary Differential Equations
Cao, J., Qi, X., & Zhao, H. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-400-1_12 · Cited by 53 · see citing works
Bailly‐Bechet, M., Braunstein, A., Pagnani, A., Weigt, M., & Zecchina, R. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-355 · Cited by 41 · see citing works
Chang, L. W., Payton, J. E., Yuan, W., Ley, T. J., Nagarajan, R., & Stormo, G. D. (2008) · Genome biology · doi:10.1186/gb-2008-9-2-r38 · Cited by 27 · see citing works
Vu, T. T., & Vohradský, J. (2009) · Genomics · doi:10.1016/j.ygeno.2009.01.006 · Cited by 17 · see citing works
Bauer, T., Eils, R., & König, R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr366 · Cited by 16 · see citing works
Tunçbağ, N., Braunstein, A., Pagnani, A., Huang, S. C., Chayes, J., Borgs, C., Zecchina, R., & Fraenkel, E. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29627-7_31 · Cited by 15 · see citing works
Youn, A., Reiss, D. J., & Stuetzle, W. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq289 · Cited by 15 · see citing works
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 · see citing works
DISCOVERING TRANSCRIPTIONAL MODULES FROM MOTIF, ChIP-CHIP AND MICROARRAY DATA
Bie, T. D., Monsieurs, P., Engelen, K., Moor, B. D., Cristianini, N., & Marchal, K. (2004) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812702456_0046 · Cited by 13 · see citing works
Zhu, J. Z., Wiener, M. C., Zhang, C., Fridman, A., Minch, E., Lum, P. Y., Sachs, J. R., & Schadt, E. E. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030069.eor · Cited by 12 · see citing works
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Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
Singh, A., Shannon, C. P., Gautier, B., Rohart, F., Vacher, M. A., Tebbutt, S. J., & Cao, K. L. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1054 · Cited by 1,079 · see citing works
Biomarker development in the precision medicine era: lung cancer as a case study
Vargas, A. J., & Harris, C. C. (2016) · Nature reviews. Cancer · doi:10.1038/nrc.2016.56 · Cited by 553 · see citing works
Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues
Lopes‐Ramos, C. M., Chen, C.-Y., Kuijjer, M. L., Paulson, J. N., Sonawane, A. R., Fagny, M., Platig, J., Glass, K., Quackenbush, J. F., & DeMeo, D. L. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107795 · Cited by 446 · see citing works
Self-citationUnderstanding Tissue-Specific Gene Regulation
Sonawane, A. R., Platig, J., Fagny, M., Chen, C.-Y., Paulson, J. N., Lopes‐Ramos, C. M., DeMeo, D. L., Quackenbush, J. F., Glass, K., & Kuijjer, M. L. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.10.001 · Cited by 410 · see citing works
Self-citationMolecular networks in Network Medicine: Development and applications
Silverman, E. K., Schmidt, H. H. H. W., Anastasiadou, E., Altucci, L., Angelini, M., Badimón, L., Balligand, J., Benincasa, G., Capasso, G., Conte, F., et al. (2020) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1489 · Cited by 211 · see citing works
Self-citationSilverman, E. K. (2019) · Annual Review of Physiology · doi:10.1146/annurev-physiol-021317-121224 · Cited by 209 · see citing works
Network Medicine in the Age of Biomedical Big Data
Sonawane, A. R., Weiss, S. T., Glass, K., & Sharma, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00294 · Cited by 189 · see citing works
Self-citationEstimating Sample-Specific Regulatory Networks
Kuijjer, M. L., Tung, M. G., Yuan, G., Quackenbush, J. F., & Glass, K. (2018) · iScience · doi:10.1016/j.isci.2019.03.021 · Cited by 179 · see citing works
Self-citationGene regulatory network inference resources: A practical overview
Mercatelli, D., Scalambra, L., Triboli, L., Ray, F., & Giorgi, F. M. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194430 · Cited by 157 · see citing works
Multi-omics integration—a comparison of unsupervised clustering methodologies
Tini, G., Marchetti, L., Priami, C., & Scott‐Boyer, M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx167 · Cited by 150 · see citing works
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Cannoodt, R., Saelens, W., Deconinck, L., & Saeys, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-24152-2 · Cited by 124 · see citing works
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GRAND: a database of gene regulatory network models across human conditions
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Self-citationXu, Q., Γεωργίου, Γ., Frölich, S., van der Sande, M., Veenstra, G. J. C., Zhou, H., & van Heeringen, S. J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab598 · Cited by 88 · see citing works
Bereczki, Z., Benczik, B., Balogh, O. M., Marton, S., Puhl, E., Pétervári, M., Váczy‐Földi, M., Papp, Z. T., Makkos, A., Glass, K., et al. (2024) · British Journal of Pharmacology · doi:10.1111/bph.17302 · Cited by 82 · see citing works
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Lao, T., Glass, K., Qiu, W., Polverino, F., Gupta, K., Morrow, J. D., Mancini, J. D., Vuong, L., Perrella, M. A., Hersh, C. P., et al. (2015) · Genome Medicine · doi:10.1186/s13073-015-0137-3 · Cited by 73 · see citing works
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Noell, G. G., Faner, R., & Agustí, A. G. N. (2018) · European Respiratory Review · doi:10.1183/16000617.0110-2017 · Cited by 60 · see citing works
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Padi, M., & Quackenbush, J. F. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0052-5 · Cited by 57 · see citing works
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Glass, K., Quackenbush, J. F., Silverman, E. K., Celli, B. R., Rennard, S. I., Yuan, G., & DeMeo, D. L. (2014) · BMC Systems Biology · doi:10.1186/s12918-014-0118-y · Cited by 57 · see citing works
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Guebila, M. B., Wang, T., Lopes‐Ramos, C. M., Fanfani, V., Weighill, D., Burkholz, R., Schlauch, D., Paulson, J. N., Altenbuchinger, M., Shutta, K. H., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02877-1 · Cited by 53 · see citing works
Self-citationRecovering dynamic networks in big static datasets
Wu, R., & Jiang, L. (2021) · Physics Reports · doi:10.1016/j.physrep.2021.01.003 · Cited by 53 · see citing works
Lu, T., & Mar, J. C. (2020) · Biology of Sex Differences · doi:10.1186/s13293-020-00335-2 · Cited by 49 · see citing works
Driesbaugh, K. H., Branchetti, E., Grau, J. B., Keeney, S. J., Glass, K., Oyama, M. A., Rioux, N., Ayoub, S., Sacks, M. S., Quackenbush, J. F., et al. (2017) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2017.12.014 · Cited by 48 · see citing works
Self-citationProceedings of the second international molecular pathological epidemiology (MPE) meeting
Ogino, S., Campbell, P. T., Nishihara, R., Phipps, A. I., Beck, A. H., Sherman, M. E., Chan, A. T., Troester, M. A., Bass, A. J., Fitzgerald, K. C., et al. (2015) · Cancer Causes & Control · doi:10.1007/s10552-015-0596-2 · Cited by 47 · see citing works
Network control principles for identifying personalized driver genes in cancer
Guo, W.-F., Zhang, S.-W., Zeng, T., Akutsu, T., & Chen, L. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz089 · Cited by 44 · see citing works
Yung, T. M., Poon, F., Liang, M., Coquenlorge, S., McGaugh, E. C., Hui, C., Wilson, M. D., Nostro, M. C., & Kim, T.-H. (2019) · Nature Communications · doi:10.1038/s41467-019-12624-5 · Cited by 44 · see citing works
DIABLO - an integrative, multi-omics, multivariate method for multi-group classificationPreprint
Singh, A., Gautier, B., Shannon, C. P., Vacher, M. A., Rohart, F., Tebutt, S. J., & Cao, K. L. (2018) · UWA Profiles and Research Repository (UWA) · doi:10.1101/067611 · Cited by 44 · see citing works
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Kuijjer, M. L., Hsieh, P., Quackenbush, J. F., & Glass, K. (2019) · BMC Cancer · doi:10.1186/s12885-019-6235-7 · Cited by 43 · see citing works
Self-citationSharma, N. V., Pellegrini, K. L., Ouellet, V., Giuste, F., Ramalingam, S. S., Watanabe, K. A., Adam-Granger, É., Fossouo, L., You, S., Freeman, M. R., et al. (2018) · Cancers · doi:10.3390/cancers10100379 · Cited by 41 · see citing works
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Sonawane, A. R., DeMeo, D. L., Quackenbush, J. F., & Glass, K. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00208-3 · Cited by 40 · see citing works
Self-citationMachine learning methods for gene regulatory network inference
Hegde, A., Nguyen, T., & Cheng, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf470 · Cited by 39 · see citing works
Biologically informed NeuralODEs for genome-wide regulatory dynamics
Hossain, I., Fanfani, V., Fischer, J., Quackenbush, J. F., & Burkholz, R. (2024) · Genome biology · doi:10.1186/s13059-024-03264-0 · Cited by 37 · see citing works
Self-citationQiu, W., Guo, F., Glass, K., Yuan, G., Quackenbush, J. F., Zhou, X., & Tantisira, K. G. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.05.052 · Cited by 36 · see citing works
Self-citationIglesias-Martinez, L. F., Kölch, W., & Santra, T. (2016) · Scientific Reports · doi:10.1038/srep37140 · Cited by 36 · see citing works
Shang, L., Smith, J. A., & Zhou, X. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008734 · Cited by 35 · see citing works
Hill, K. E., Kelly, A. D., Kuijjer, M. L., Barry, W. T., Rattani, A., Garbutt, C. C., Kissick, H. T., Janeway, K. A., Pérez‐Atayde, A. R., Goldsmith, J. D., et al. (2017) · Journal of Hematology & Oncology · doi:10.1186/s13045-017-0465-4 · Cited by 34 · see citing works
Self-citationGene regulatory networks reveal sex difference in lung adenocarcinoma
Saha, E., Guebila, M. B., Fanfani, V., Fischer, J., Shutta, K. H., Mandros, P., DeMeo, D. L., Quackenbush, J. F., & Lopes‐Ramos, C. M. (2024) · Biology of Sex Differences · doi:10.1186/s13293-024-00634-y · Cited by 33 · see citing works
Self-citationLoers, J. U., & Vermeirssen, V. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae382 · Cited by 33 · see citing works
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Shutta, K. H., Vito, R. D., Scholtens, D. M., & Balasubramanian, R. (2022) · Statistics in Medicine · doi:10.1002/sim.9546 · Cited by 31 · see citing works
DeMeo, D. L. (2024) · Annual Review of Physiology · doi:10.1146/annurev-physiol-042022-014322 · Cited by 30 · see citing works
PUMA: PANDA Using MicroRNA Associations
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Self-citationGene Targeting in Disease Networks
Weighill, D. A., Guebila, M. B., Glass, K., Platig, J., Yeh, J. J., & Quackenbush, J. F. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.649942 · Cited by 30 · see citing works
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Djordjevic, D., Yang, A., Zadoorian, A., Rungrugeecharoen, K., & Ho, J. W. K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111661 · Cited by 29 · see citing works
Padi, M., & Quackenbush, J. F. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0228-1 · Cited by 29 · see citing works
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Park, H., & Weiss, S. T. (2020) · Allergy Asthma and Immunology Research · doi:10.4168/aair.2020.12.3.399 · Cited by 29 · see citing works
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Hurgobin, B., de Jong, E., & Bosco, A. (2018) · Respirology · doi:10.1111/resp.13401 · Cited by 28 · see citing works
Fagny, M., Kuijjer, M. L., Stam, M. E., Joets, J., Turc, O., Rozière, J., Pateyron, S., Venon, A., & Vitte, C. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.606285 · Cited by 28 · see citing works
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Kwon, M. S., Lee, B. T., Lee, S. Y., & Kim, H. U. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.02.014 · Cited by 27 · see citing works
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Jones, E. F., Haldar, A., Oza, V. H., & Lasseigne, B. N. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad019 · Cited by 27 · see citing works
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Al‐Harazi, O., Insaif, S. A., Al-Ajlan, M., Kaya, N., Dzimiri, N., & Çolak, D. (2015) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2015.11.002 · Cited by 27 · see citing works
Ge, S., Liu, J., Cheng, Y., Meng, X., & Wang, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac500 · Cited by 26 · see citing works
Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme
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Shutta, K. H., Weighill, D. A., Burkholz, R., Guebila, M. B., DeMeo, D. L., Zacharias, H. U., Quackenbush, J. F., & Altenbuchinger, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1157 · Cited by 23 · see citing works
Self-citationPrior Knowledge Driven Joint NMF Algorithm for ceRNA Co-Module Identification
Deng, J., Kong, W., Wang, S., Mou, X., & Zeng, W. (2018) · International Journal of Biological Sciences · doi:10.7150/ijbs.27555 · Cited by 23 · see citing works
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Pandey, A. K., & Loscalzo, J. (2023) · Nature Reviews Nephrology · doi:10.1038/s41581-023-00705-0 · Cited by 22 · see citing works
Estimating gene regulatory networks with pandaR
Schlauch, D., Paulson, J. N., Young, A. T., Glass, K., & Quackenbush, J. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx139 · Cited by 22 · see citing works
Self-citationUsing single cell atlas data to reconstruct regulatory networks
Song, Q., Ruffalo, M., & Bar‐Joseph, Z. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad053 · Cited by 21 · see citing works
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Truong, T. T. T., Liu, Z. S.-J., Panizzutti, B., Kim, J. H., Dean, O. M., Berk, M., & Walder, K. R. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01805-6 · Cited by 21 · see citing works
Integrative computational epigenomics to build data-driven gene regulation hypotheses
Chen, T., & Tyagi, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa064 · Cited by 21 · see citing works
Genetic Screens Identify a Context-Specific PI3K/p27Kip1 Node Driving Extrahepatic Biliary Cancer
Falcomatà, C., Bärthel, S., Ulrich, A., Diersch, S., Veltkamp, C., Rad, L., Boniolo, F., Solar, M., Steiger, K., Seidler, B., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0209 · Cited by 20 · see citing works
Tao, W., Radstake, T. R. D. J., & Pandit, A. (2022) · Communications Biology · doi:10.1038/s42003-021-02991-5 · Cited by 20 · see citing works
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Weighill, D. A., Guebila, M. B., Lopes‐Ramos, C. M., Glass, K., Quackenbush, J. F., Platig, J., & Burkholz, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17230 · Cited by 20 · see citing works
Self-citationOsorio, D., Capasso, A., Eckhardt, S. G., Giri, U. P., Somma, A. J., Pitts, T. M., Lieu, C. H., Messersmith, W. A., Bagby, S. M., Singh, H., et al. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00597-5 · Cited by 19 · see citing works
Min, L., Zhang, C., Qu, L., Huang, J., Jiang, L., Liu, J., Pinello, L., Yuan, G., & Shou, C. (2017) · Oncotarget · doi:10.18632/oncotarget.15749 · Cited by 19 · see citing works
Self-citationAbiotic-stress tolerance in plants-system biology approach
Pandey, P., Srivastava, S., Pandey, A. K., & Dubey, R. S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818204-8.00025-4 · Cited by 19 · see citing works
Predicting genotype-specific gene regulatory networksPreprint
Weighill, D. A., Guebila, M. B., Glass, K., Quackenbush, J. F., & Platig, J. (2022) · Genome Research · doi:10.1101/gr.275107.120 · Cited by 19 · see citing works
Self-citationEstimating drivers of cell state transitions using gene regulatory network models
Schlauch, D., Glass, K., Hersh, C. P., Silverman, E. K., & Quackenbush, J. F. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0517-y · Cited by 18 · see citing works
Self-citationDoherty, M. M., Metcalfe, T., Guardino, E., Peters, E. J., & Ramage, L. (2016) · Annals of Oncology · doi:10.1093/annonc/mdw165 · Cited by 18 · see citing works
Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases
Sonawane, A. R., Aïkawa, E., & Aikawa, M. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.873582 · Cited by 17 · see citing works
PyPanda: a Python package for gene regulatory network reconstruction
van IJzendoorn, D. G. P., Glass, K., Quackenbush, J. F., & Kuijjer, M. L. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw422 · Cited by 17 · see citing works
Self-citationImmune network dysregulation precedes clinical diagnosis of asthma
Chang, Y.-S., Turturice, B. A., Schott, C. A., Finn, P. W., & Perkins, D. L. (2020) · Scientific Reports · doi:10.1038/s41598-020-69494-x · Cited by 15 · see citing works
Cao, B., Zhao, Y. C., Zhang, Z., Li, H., Xing, J., Guo, S., Qiu, X., Zhang, S., Min, L., & Zhu, S. (2018) · International Journal of Oncology · doi:10.3892/ijo.2018.4519 · Cited by 15 · see citing works
Sex-Biased Regulation of Extracellular Matrix Genes in Chronic Obstructive Pulmonary Disease
Lopes‐Ramos, C. M., Shutta, K. H., Ryu, M. H., Huang, Y., Saha, E., Ziniti, J. P., Chase, R., Hobbs, B. D., Yun, J. H., Castaldi, P. J., et al. (2024) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2024-0226oc · Cited by 14 · see citing works
Self-citationPerspective on recent developments and challenges in regulatory and systems genomics
Zeitlinger, J., Roy, S., Ay, F., Mathelier, A., Medina-Rivera, A., Mahony, S., Sinha, S. K., & Ernst, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf106 · Cited by 14 · see citing works
Systematic analysis of differentially expressed ZmMYB genes related to drought stress in maize
Zhang, P., Qiu, X., Fu, J., Wang, G., Li, W., & Wang, T. (2021) · Physiology and Molecular Biology of Plants · doi:10.1007/s12298-021-01013-2 · Cited by 14 · see citing works
Patterns of selection across gene regulatory networks
McDonald, J. M. C., & Reed, R. D. (2022) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2022.03.029 · Cited by 14 · see citing works
Construction of Transcriptional Regulatory Network of Alzheimer’s Disease Based on PANDA Algorithm
Ding, J., Kong, W., Mou, X., & Wang, S. (2018) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-018-0297-0 · Cited by 14 · see citing works
Contrastive latent variable modeling with application to case-control sequencing experiments
Jones, A., Townes, F. W., Li, D., & Engelhardt, B. E. (2022) · The Annals of Applied Statistics · doi:10.1214/21-aoas1534 · Cited by 14 · see citing works
Diet-induced weight loss leads to a switch in gene regulatory network control in the rectal mucosa
Vargas, A. J., Quackenbush, J. F., & Glass, K. (2016) · Genomics · doi:10.1016/j.ygeno.2016.08.001 · Cited by 14 · see citing works
Self-citationHigher-order correction of persistent batch effects in correlation networks
Micheletti, S., Schlauch, D., Quackenbush, J. F., & Guebila, M. B. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae531 · Cited by 13 · see citing works
Self-citationLee, H.-Y., Jeon, Y., Kim, Y. K., Jang, J. Y., Cho, Y. S., Bhak, J., & Cho, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-91811-1 · Cited by 13 · see citing works
Toward an understanding of the relation between gene regulation and 3D genome organization
Tian, H., Yang, Y., Liu, S., Quan, H., & Gao, Y. Q. (2020) · Quantitative Biology · doi:10.1007/s40484-020-0221-6 · Cited by 13 · see citing works
Bayesian inference of sample-specific coexpression networksPreprint
Saha, E., Fanfani, V., Mandros, P., Guebila, M. B., Fischer, J., Shutta, K. H., DeMeo, D. L., Lopes‐Ramos, C. M., & Quackenbush, J. F. (2024) · Genome Research · doi:10.1101/gr.279117.124 · Cited by 13 · see citing works
Self-citationComputational approaches to understand transcription regulation in development
van der Sande, M., Frölich, S., & van Heeringen, S. J. (2023) · Biochemical Society Transactions · doi:10.1042/bst20210145 · Cited by 12 · see citing works
Schnepp, P. M., Ahmed, A. A., Escara-Wilke, J. F., Dai, J., Shelley, G., Keller, J. M., Mizokami, A., & Keller, E. T. (2021) · BMC Cancer · doi:10.1186/s12885-021-09048-0 · Cited by 12 · see citing works
Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues
Pélissier, A., Laragione, T., Gulko, P. S., & Martínez, M. R. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1428773 · Cited by 11 · see citing works
Whitlock, J. H., Soelter, T. M., Howton, T. C., Wilk, E. J., Oza, V. H., & Lasseigne, B. N. (2023) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.18001 · Cited by 11 · see citing works
- Authors
- Glass, K., Huttenhower, C., Quackenbush, J., & Yuan, G.-C.
- Affiliations
- Harvard University (US)
- Corresponding author
- Yuan, Guo-Cheng
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-05-31
- Volume
- 8
- Issue
- 5
- Pages
- e64832
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3669401
- OpenAlex
- W2020672230
- Cited by
- 266
- References
- 64
- Citations by year
- 2026: 24 · 2025: 26 · 2024: 56 · 2023: 22 · 2022: 24 · 2021: 29 · 2020: 24 · 2019: 15 · 2018: 11 · 2017: 12 · 2016: 12 · 2015: 7 · 2014: 4
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (1053486)
- National Institutes of Health (5r01hl111759-04)
- National Heart, Lung, and Blood Institute (R01-HL111759, P01HL105339)
- Division of Biological Infrastructure (DBI-1053486)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- regulatory network reconstruction, data integration, message passing, protein-protein interaction, gene expression, sequence motifs, yeast, genome-scale data, network prediction
- MeSH terms
- Algorithms, Computer Simulation, Models, Biological, Saccharomyces cerevisiae, Reproducibility of Results, Signal Transduction, Gene Regulatory Networks, Protein Interaction Maps
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