The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
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.
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
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?
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
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
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
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,855
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
BioGRID: a general repository for interaction datasets
Stark, C. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj109 · Cited by 3,960
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
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
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
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., & Friedman, N. (2003) · Nature Genetics · doi:10.1038/ng1165 · Cited by 1,678
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
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
Flagged first, then by citations
Cited by
Sorted by citations
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 995
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 1,124
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 562
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 462
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 425
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 218
Self-citationSilverman, E. K. (2019) · Annual Review of Physiology · doi:10.1146/annurev-physiol-021317-121224 · Cited by 220
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 197
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 194
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 169
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 157
Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells
Cannoodt, R., Saelens, W., Deconinck, L., & Saeys, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-24152-2 · Cited by 124
Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism
Lopes‐Ramos, C. M., Kuijjer, M. L., Ogino, S., Fuchs, C. S., DeMeo, D. L., Glass, K., & Quackenbush, J. F. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0454 · Cited by 122
Self-citationCurrent and future directions in network biology
Žitnik, M., Li, M. M., Wells, A., Glass, K., Gysi, D. M., Krishnan, A., Murali, T. M., Predrag Radivojac, A., Roy, S., Baudot, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae099 · Cited by 128
Self-citationA paradigm shift in medicine: A comprehensive review of network-based approaches
Conte, F., Fiscon, G., Licursi, V., Bizzarri, D., D'Antò, T., Farina, L., & Paci, P. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194416 · Cited by 99
GRAND: a database of gene regulatory network models across human conditions
Guebila, M. B., Lopes‐Ramos, C. M., Weighill, D. A., Sonawane, A. R., Burkholz, R., Shamsaei, B., Platig, J., Glass, K., Kuijjer, M. L., & Quackenbush, J. F. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab778 · Cited by 92
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 89
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 86
Self-citationNetwork-Based Approaches to Explore Complex Biological Systems towards Network Medicine
Fiscon, G., Conte, F., Farina, L., & Paci, P. (2018) · Genes · doi:10.3390/genes9090437 · Cited by 85
Big data: Historic advances and emerging trends in biomedical research
Cremin, C. J., Dash, S., & Huang, X. (2022) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2022.02.004 · Cited by 91
(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2893170 · Cited by 78
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 75
Self-citationTranscriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment
Serra, A., Fratello, M., Cattelani, L., Liampa, I., Melagraki, G., Kohonen, P. J., Nymark, P. E. H., Federico, A., Kinaret, P., Jagiełło, K., et al. (2020) · Nanomaterials · doi:10.3390/nano10040708 · Cited by 76
Methods for multi-omic data integration in cancer research
Lemus, E. H., & Ochoa, S. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1425456 · Cited by 72
Regulatory network changes between cell lines and their tissues of origin
Lopes‐Ramos, C. M., Paulson, J. N., Chen, C.-Y., Kuijjer, M. L., Fagny, M., Platig, J., Sonawane, A. R., DeMeo, D. L., Quackenbush, J. F., & Glass, K. (2017) · BMC Genomics · doi:10.1186/s12864-017-4111-x · Cited by 71
Self-citationNetwork-based approaches for modeling disease regulation and progression
Galindez, G., Sadegh, S., Baumbach, J., Kacprowski, T., & List, M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.022 · Cited by 74
A network model for angiogenesis in ovarian cancer
Glass, K., Quackenbush, J. F., Spentzos, D., Haibe‐Kains, B., & Yuan, G. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0551-y · Cited by 66
Self-citationContext Specific and Differential Gene Co-expression Networks via Bayesian Biclustering
Gao, C., McDowell, I. C., Zhao, S., Brown, C. D., & Engelhardt, B. E. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004791 · Cited by 66
Strengths and Opportunities of Network Medicine in Cardiovascular Diseases
Benincasa, G., Marfella, R., Mura, N. D., Schiano, C., & Claudio Napoli, A. (2019) · Circulation Journal · doi:10.1253/circj.cj-19-0879 · Cited by 61
From systems biology to P4 medicine: applications in respiratory medicine
Noell, G. G., Faner, R., & Agustí, A. G. N. (2018) · European Respiratory Review · doi:10.1183/16000617.0110-2017 · Cited by 63
Detecting phenotype-driven transitions in regulatory network structure
Padi, M., & Quackenbush, J. F. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0052-5 · Cited by 58
Self-citationSexually-dimorphic targeting of functionally-related genes in COPD
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 59
Self-citationThe Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks
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 55
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 54
Lu, T., & Mar, J. C. (2020) · Biology of Sex Differences · doi:10.1186/s13293-020-00335-2 · Cited by 52
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 49
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
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
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 45
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
lionessR: single sample network inference in R
Kuijjer, M. L., Hsieh, P., Quackenbush, J. F., & Glass, K. (2019) · BMC Cancer · doi:10.1186/s12885-019-6235-7 · Cited by 43
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
Constructing gene regulatory networks using epigenetic data
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 45
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 44
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 44
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
Self-citationIglesias-Martinez, L. F., Kölch, W., & Santra, T. (2016) · Scientific Reports · doi:10.1038/srep37140 · Cited by 36
Shang, L., Smith, J. A., & Zhou, X. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008734 · Cited by 35
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
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
Self-citationLoers, J. U., & Vermeirssen, V. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae382 · Cited by 39
Gaussian graphical models with applications to omics analyses
Shutta, K. H., Vito, R. D., Scholtens, D. M., & Balasubramanian, R. (2022) · Statistics in Medicine · doi:10.1002/sim.9546 · Cited by 33
DeMeo, D. L. (2024) · Annual Review of Physiology · doi:10.1146/annurev-physiol-042022-014322 · Cited by 30
PUMA: PANDA Using MicroRNA Associations
Kuijjer, M. L., Fagny, M., Marin, A., Quackenbush, J. F., & Glass, K. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa571 · Cited by 30
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
Self-citationHow difficult is inference of mammalian causal gene regulatory networks?
Djordjevic, D., Yang, A., Zadoorian, A., Rungrugeecharoen, K., & Ho, J. W. K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111661 · Cited by 29
Padi, M., & Quackenbush, J. F. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0228-1 · Cited by 29
Self-citationUnderstanding the Molecular Mechanisms of Asthma through Transcriptomics
Park, H., & Weiss, S. T. (2020) · Allergy Asthma and Immunology Research · doi:10.4168/aair.2020.12.3.399 · Cited by 30
Insights into respiratory disease through bioinformatics
Hurgobin, B., de Jong, E., & Bosco, A. (2018) · Respirology · doi:10.1111/resp.13401 · Cited by 28
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
Modeling regulatory networks using machine learning for systems metabolic engineering
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
Quantifying transcriptome diversity: a review
Jones, E. F., Haldar, A., Oza, V. H., & Lasseigne, B. N. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad019 · Cited by 28
Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network
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 28
Ge, S., Liu, J., Cheng, Y., Meng, X., & Wang, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac500 · Cited by 26
Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme
Lopes‐Ramos, C. M., Belova, T., Brunner, T. H., Guebila, M. B., Osorio, D., Quackenbush, J. F., & Kuijjer, M. L. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-0730 · Cited by 26
Self-citationDRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks
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
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
Network medicine: an approach to complex kidney disease phenotypes
Pandey, A. K., & Loscalzo, J. (2023) · Nature Reviews Nephrology · doi:10.1038/s41581-023-00705-0 · Cited by 23
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
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 23
Network-based drug repurposing for schizophrenia
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 22
Integrative computational epigenomics to build data-driven gene regulation hypotheses
Chen, T., & Tyagi, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa064 · Cited by 21
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 21
Tao, W., Radstake, T. R. D. J., & Pandit, A. (2022) · Communications Biology · doi:10.1038/s42003-021-02991-5 · Cited by 21
Gene Regulatory Network Inference as Relaxed Graph Matching
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
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 25
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
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
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 22
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 19
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
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 18
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
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 17
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
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
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 18
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 15
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
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
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 17
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
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 14
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
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
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
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
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
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
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
Metadata
- 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
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
