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Network medicine: a network-based approach to human disease
Open accessAbstract
Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
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“Few genes act in isolation but rather affect the function of other genes to influence a particular phenotype via in cellular networks or pathways (Barabasi et al., 2011; Geschwind and Konopka, 2009).”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Barabasi et al., 2011
In the citing paper
“Communities of co-expressed proteins can be linked to disease processes, and the most strongly correlated proteins, or “hubs,” within these co-expression modules are enriched in key drivers of disease pathogenesis5–10.”
In the citing paper
“Machine learning has been used to analyze high-throughput molecular interactomes to unravel genetic architecture perturbed in disease12,13 and help design therapies to target them14.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“This evaluation method aligns with the approach predominantly used in the literature12,14–16,18–20.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“TxGNN leverages an additional metric learning component for zero-shot prediction, capitalizing on the insight that diseases can share disease-associated genetic and genomic networks10,14 and, thus, medical knowledge of disease-associated networks can be transferred by the model from well-annotated diseases to other diseases to enhance predictions on diseases with limited treatment options (Fig. 2a”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [14]
In the citing paper
“In protein interaction networks, for instance, mutations in interacting proteins often lead to similar diseases [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Further, essential genes are often found in hubs of a molecular network whereas non-essential genes (e.g., those associated with disease) are located on the periphery [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Given by the shared components and disease module hypotheses [13], cellular components associated with the same phenotype tend to cluster in the same network neighborhood [16].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Thus, the network parsimony principle dictates that shortest molecular paths between known disease-associated components tend to correlate with causal molecular pathways [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“First, interacting entities are typically more similar than non-interacting entities, as implicated by the local hypothesis [13].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“The organizing principles governing networks link network structure to molecular phenotypes, biological roles, disease, and health, thus providing the conceptual grounding that, we argue, can explain the successes (and limitations) of graph representation learning and inform future development of the field.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Because such processes are costly and time-consuming, in silico approaches have been adopted into the drug discovery pipeline.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
In the citing paper
“Modern drug discovery requires elucidating a candidate drug’s chemical structure, identifying its drug targets, quantifying its efficacy and toxicity, and detecting its potential side effects [13, 14, 32, 164].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [13]
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Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Self-citationStatistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
Self-citationCommunity structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855
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
Self-citationNetwork motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Uncovering the overlapping community structure of complex networks in nature and society
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005) · Nature · doi:10.1038/nature03607 · Cited by 5,373
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
Self-citationThe Large-Scale Organization of Metabolic Networks
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972
Self-citation
Flagged first, then by citations
1 of the 140 references couldn’t be matched to a known paper.
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Reactive oxygen species (ROS) as pleiotropic physiological signalling agents
Sies, H., & Jones, D. P. (2020) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-0230-3 · Cited by 5,335
Targeting hypoxia in cancer therapy
Wilson, W. R., & Hay, M. P. (2011) · Nature Reviews Cancer · doi:10.1038/nrc3064 · Cited by 3,172
The structure and dynamics of multilayer networks
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125
Keystone taxa as drivers of microbiome structure and functioning
Banerjee, S., Schlaeppi, K., & van der Heijden, M. G. A. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0024-1 · Cited by 3,148
The structure and dynamics of multilayer networksPreprint
Wangk, Z., & Zaninm, M. (2016) · Cited by 3,001
Bassett, D. S., & Sporns, O. (2017) · Nature Neuroscience · doi:10.1038/nn.4502 · Cited by 2,398
Newman, M. E. J. (2018) · doi:10.1093/oso/9780198805090.001.0001 · Cited by 2,453
Interactome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771
Self-citationDefining roles of specific reactive oxygen species (ROS) in cell biology and physiology
Sies, H., Belousov, V. V., Chandel, N. S., Davies, M. J., Jones, D. P., Mann, G. E., Murphy, M. P., Yamamoto, M., & Winterbourn, C. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00456-z · Cited by 1,735
STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data
Szklarczyk, D., Santos, A., von Mering, C., Jensen, L. J., Bork, P., & Kuhn, M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1277 · Cited by 1,740
Uncovering disease-disease relationships through the incomplete interactome
Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabási, A.-L. (2015) · Science · doi:10.1126/science.1257601 · Cited by 1,546
Self-citationComparing network structures on three aspects: A permutation test.
van Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., Borsboom, D., & Waldorp, L. (2022) · Psychological Methods · doi:10.1037/met0000476 · Cited by 1,634
A Proteome-Scale Map of the Human Interactome Network
Rolland, T., Taşan, M., Charloteaux, B., Pevzner, S. J., Zhong, Q., Sahni, N., Yi, S. S., Lemmens, I. H., Fontanillo, C., Mosca, R., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.050 · Cited by 1,381
Self-citationBanerjee, S., Walder, F., Büchi, L., Meyer, M., Valzano‐Held, A., Gattinger, A., Keller, T., Charles, R., & van der Heijden, M. G. A. (2019) · The ISME Journal · doi:10.1038/s41396-019-0383-2 · Cited by 1,400
Traditional Chinese medicine network pharmacology: theory, methodology and application
Li, S., & Zhang, B. (2013) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(13)60037-0 · Cited by 1,348
Deep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,288
Genetic architectures of psychiatric disorders: the emerging picture and its implications
Sullivan, P. F., Daly, M. J., & O'Donovan, M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3240 · Cited by 1,177
Treatable traits: toward precision medicine of chronic airway diseases
Agustí, A. G. N., Bel, E. H., Thomas, M. D., Vogelmeier, C. F., Brusselle, G., Holgate, S. T., Humbert, M. J. C., Jones, P., Gibson, P. G., Vestbo, J., et al. (2016) · European Respiratory Journal · doi:10.1183/13993003.01359-2015 · Cited by 1,122
Pleiotropy in complex traits: challenges and strategies
Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., & Smoller, J. W. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3461 · Cited by 1,085
After asthma: redefining airways diseases
Pavord, I. D., Beasley, R., Agusti, A., Anderson, G. P., Bel, E., Brusselle, G., Cullinan, P., Custovic, A., Ducharme, F. M., Fahy, J. V., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(17)30879-6 · Cited by 1,073
Johnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 911
miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology
Chang, L., Zhou, G., Soufan, O., & Xia, J. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa467 · Cited by 886
Agustí, A. G. N., Edwards, L. D., Rennard, S. I., MacNee, W., Tal‐Singer, R., Miller, B. E., Vestbo, J., Lomas, D. A., Calverley, P. M. A., Wouters, E. F. �., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037483 · Cited by 873
Association of Symptom Network Structure With the Course of Depression
van Borkulo, C. D., Boschloo, L., Borsboom, D., Penninx, B. W. J. H., Waldorp, L., & Schoevers, R. A. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.2079 · Cited by 883
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
Cheng, F., Liu, C., Jiang, J., Lü, W., Li, W., Liu, G., Zhou, W., Huang, J. Y., & Tang, Y. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002503 · Cited by 827
Network-based prediction of drug combinations
Cheng, F., Kovács, I. A., & Barabási, A.-Ĺ. (2019) · Nature Communications · doi:10.1038/s41467-019-09186-x · Cited by 819
Self-citationUsing graph theory to analyze biological networks
Pavlopoulos, G. A., Secrier, M., Moschopoulos, C. N., Soldatos, T. G., Κοσσίδα, Σ., Aerts, J. A., Schneider, R., & Bagos, P. G. (2011) · BioData Mining · doi:10.1186/1756-0381-4-10 · Cited by 821
Validating therapeutic targets through human genetics
Plenge, R. M., Scolnick, E. M., & Altshuler, D. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4051 · Cited by 712
Ageing and the epidemiology of multimorbidity
Divo, M. J., Martinez, C. H., & Mannino, D. M. (2014) · European Respiratory Journal · doi:10.1183/09031936.00059814 · Cited by 705
Zhang, P., Zhang, D., Zhou, W., Wang, L., Wang, B., Zhang, T., & Li, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad518 · Cited by 719
Robustness and resilience of complex networks
Artime, O., Grassia, M., Domenico, M. D., Gleeson, J. P., Makse, H. A., Mangioni, G., Perc, M., & Radicchi, F. (2024) · Nature Reviews Physics · doi:10.1038/s42254-023-00676-y · Cited by 714
Co-regulatory networks of human serum proteins link genetics to disease
Emilsson, V., Ilkov, M., Lamb, J. R., Finkel, N., Gudmundsson, E. F., Pitts, R., Hoover, H., Gudmundsdottir, V., Horman, S. R., Aspelund, T., et al. (2018) · Science · doi:10.1126/science.aaq1327 · Cited by 680
Mutual exclusivity analysis identifies oncogenic network modulesPreprint
Ciriello, G., Cerami, E. G., Sander, C., & Schultz, N. D. (2011) · Genome Research · doi:10.1101/gr.125567.111 · Cited by 678
A network-biology perspective of microRNA function and dysfunction in cancer
Bracken, C. P., Scott, H. S., & Goodall, G. J. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.134 · Cited by 688
Network propagation: a universal amplifier of genetic associations
Cowen, L., Ideker, T., Raphael, B. J., & Sharan, R. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.38 · Cited by 669
Control principles of complex systems
Liu, Y., & Barabási, A.-Ĺ. (2016) · Reviews of Modern Physics · doi:10.1103/revmodphys.88.035006 · Cited by 650
Self-citationHuman symptoms–disease network
Zhou, X., Menche, J., Barabási, A.-Ĺ., & Sharma, A. (2014) · Nature Communications · doi:10.1038/ncomms5212 · Cited by 659
Self-citationTranscription Factor NRF2 as a Therapeutic Target for Chronic Diseases: A Systems Medicine Approach
Cuadrado, A., Manda, G., Hassan, A., Alcaraz, M. J., Barbas, C., Daiber, A., Ghezzi, P., León, R., López, M. G., Oliva, B., et al. (2018) · Pharmacological Reviews · doi:10.1124/pr.117.014753 · Cited by 632
Network-based in silico drug efficacy screening
Guney, E., Menche, J., Vidal, M., & Barábasi, A.-L. (2016) · Nature Communications · doi:10.1038/ncomms10331 · Cited by 625
Self-citationReview of Drug Repositioning Approaches and Resources
Xue, H., Li, J., Xie, H., & Wang, Y. (2018) · International Journal of Biological Sciences · doi:10.7150/ijbs.24612 · Cited by 613
Savelieff, M. G., Nam, G., Kang, J., Lee, H. J., Lee, M., & Lim, M. H. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00138 · Cited by 612
Fabbri, E., Zoli, M., González‐Freire, M., Salive, M. E., Studenski, S. A., & Ferrucci, L. (2015) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2015.03.013 · Cited by 589
Digital twins to personalize medicine
on behalf of the Swedish Digital Twin Consortium, Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D. R., Gustafsson, M., Jörnsten, R., Lee, E. J., Li, X., et al. (2020) · Genome Medicine · doi:10.1186/s13073-019-0701-3 · Cited by 566
From big data analysis to personalized medicine for all: challenges and opportunities
Alyass, A., Turcotte, M., & Meyre, D. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0108-y · Cited by 575
Xin, W. A. N. G., Wang, Z., Zheng, J., & Li, S. (2021) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(21)60001-8 · Cited by 559
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and GaugesPreprint
Bronstein, M. M., Bruna, J., Cohen, T., & Veličković, P. (2021) · arXiv (Cornell University) · doi:10.4230/oasics.dx.2026.1 · Cited by 2,094
Applications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545
Integrative approaches for finding modular structure in biological networks
Mitra, K., Carvunis, A.-R., Ramesh, S. K., & Ideker, T. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3552 · Cited by 535
Overcoming cancer therapeutic bottleneck by drug repurposing
Zhang, Z., Zhou, L., Xie, N., Nice, E. C., Zhang, T., Cui, Y., & Huang, C. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00213-8 · Cited by 544
Observability of complex systems
Liu, Y., Slotine, J.-J. E., & Barabási, A.-Ĺ. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215508110 · Cited by 526
Self-citationTopological analysis and interactive visualization of biological networks and protein structures
Doncheva, N. T., Assenov, Y., Domingues, F. S., & Albrecht, M. (2012) · Nature Protocols · doi:10.1038/nprot.2012.004 · Cited by 511
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Kovács, I. A., Luck, K., Spirohn, K., Wang, Y., Pollis, C., Schlabach, S., Bian, W., Kim, D., Kishore, N., Hao, T., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09177-y · Cited by 480
Self-citationIntroduction to current and future protein therapeutics: A protein engineering perspective
Carter, P. J. (2011) · Experimental Cell Research · doi:10.1016/j.yexcr.2011.02.013 · Cited by 489
Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways
Chen, L., Zhang, Y.-H., Wang, S., Zhang, Y., Huang, T., & Cai, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184129 · Cited by 467
HINT: High-quality protein interactomes and their applications in understanding human disease
Das, J., & Yu, H. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-92 · Cited by 462
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Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452
“Few genes act in isolation but rather affect the function of other genes to influence a particular phenotype via in-cellular networks or pathways (Barabási et al., 2011; Geschwind and Konopka, 2009).”
BackgroundMethods for the integration of multi-omics data: mathematical aspects
Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., & Milanesi, L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0857-9 · Cited by 449
Artificial intelligence in cancer target identification and drug discovery
You, Y., Lai, X., Pan, Y. F., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00994-0 · Cited by 499
Drug combination therapy increases successful drug repositioning
Sun, W., Sanderson, P. E. J., & Zheng, W. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.05.015 · Cited by 445
Tissue-specific alternative splicing remodels protein-protein interaction networks
Ellis, J. D., Barrios-Rodiles, M., Çolak, R., Irimia, M., Kim, T., Calarco, J. A., Wang, X., Pan, Q., O'Hanlon, D., Kim, P. M., et al. (2012) · Molecular Cell · doi:10.1016/j.molcel.2012.05.037 · Cited by 431
Network science of biological systems at different scales: A review
Gosak, M., Markovič, R., Dolenšek, J., Rupnik, M. S., Marhl, M., Stožer, A., & Perc, M. (2017) · Physics of Life Reviews · doi:10.1016/j.plrev.2017.11.003 · Cited by 428
Digital twin in healthcare: Recent updates and challenges
Sun, T., He, X., & Li, Z. (2023) · Digital Health · doi:10.1177/20552076221149651 · Cited by 421
Computational tools for prioritizing candidate genes: boosting disease gene discovery
Moreau, Y., & Tranchevent, L.-C. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3253 · Cited by 417
Liby, K. T., & Sporn, M. B. (2012) · Pharmacological Reviews · doi:10.1124/pr.111.004846 · Cited by 410
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Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., Goy, A., & Suh, K. S. (2015) · Journal of Clinical Bioinformatics · doi:10.1186/s13336-015-0019-3 · Cited by 408
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Zhang, X., Li, L., Liu, L., & Tang, M. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.690049 · Cited by 413
A multidimensional systems biology analysis of cellular senescence in aging and disease
Avelar, R. A., Ortega, J. G., Tacutu, R., Tyler, E. J., Bennett, D., Binetti, P., Budovsky, A., Chatsirisupachai, K., Johnson, E., Murray, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-01990-9 · Cited by 399
Molecular characterization and clinical relevance of m6A regulators across 33 cancer types
Li, Y., Xiao, J. Y., Bai, J., Tian, Y., Qu, Y., Chen, X., Wang, Q., Li, X., Zhang, Y., & Xu, J. (2019) · Molecular Cancer · doi:10.1186/s12943-019-1066-3 · Cited by 399
Prioritization of disease microRNAs through a human phenome-microRNAome network
Jiang, Q., Hao, Y., Wang, G., Juan, L., Zhang, T., Teng, M., Liu, Y., & Wang, Y. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-s1-s2 · Cited by 392
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Κάτσιλα, Θ., Spyroulias, G. A., Patrinos, G. P., & Matsoukas, M. (2016) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.04.004 · Cited by 408
Ghiassian, S. D., Menche, J., & Barabási, A.-Ĺ. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004120 · Cited by 394
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Krewski, D. R., Andersen, M. E., Tyshenko, M. G., Krishnan, K., Härtung, T., Boekelheide, K., Wambaugh, J. F., Jones, D. P., Whelan, M., Thomas, R. S., et al. (2019) · Archives of Toxicology · doi:10.1007/s00204-019-02613-4 · Cited by 401
Vijg, J., & Suh, Y. (2013) · Annual Review of Physiology · doi:10.1146/annurev-physiol-030212-183715 · Cited by 399
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Taneera, J., Lang, S., Sharma, A., Fadista, J., Zhou, Y., Ahlqvist, E., Jonsson, A. E., Lyssenko, V., Vikman, P. J., Hansson, O., et al. (2012) · Cell Metabolism · doi:10.1016/j.cmet.2012.06.006 · Cited by 386
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Hofmann, S. G., Curtiss, J., & McNally, R. J. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616639283 · Cited by 366
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Leopold, J. A., & Loscalzo, J. (2018) · Circulation Research · doi:10.1161/circresaha.117.310782 · Cited by 380
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Boezio, B., Audouze, K., Ducrot, P., & Taboureau, O. (2017) · Molecular Informatics · doi:10.1002/minf.201700048 · Cited by 364
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Zhao, S., & Iyengar, R. (2011) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010611-134520 · Cited by 366
Realistic control of network dynamics
Cornelius, S. P., Kath, W. L., & Motter, A. E. (2013) · Nature Communications · doi:10.1038/ncomms2939 · Cited by 340
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Metadata
- Authors
- Barabási, A.-L., Gulbahce, N., & Loscalzo, J.
- Affiliations
- Northeastern University (US)
- Brigham and Women's Hospital (US)
- Venue
- Nature Reviews Genetics
- Published
- 2010-12-17
- Volume
- 12
- Issue
- 1
- Pages
- 56-68
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3140052
- OpenAlex
- W2062533676
- Cited by
- 5,034
- References
- 140
- Citations by year
- 2026: 421 · 2025: 322 · 2024: 296 · 2023: 299 · 2022: 346 · 2021: 390 · 2020: 384 · 2019: 356 · 2018: 383 · 2017: 326 · 2016: 311 · 2015: 324 · 2014: 309 · 2013: 272 · 2012: 217
- FWCI
- 15.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (HL81587, HL 70819, U54HL070819, P01HL081587, R21HL089734, R01HL061795, HL-48743, P01HL048743, HL-061795, R37HL061795)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- network medicine, molecular networks, disease-associated genes, disease-associated mutations, therapeutic target, biomarkers, disease modules, complex diseases, genome-wide association study, full genome sequencing
- MeSH terms
- Disease, Humans, Gene Expression Profiling, Databases, Genetic, Gene Regulatory Networks, Metabolic Networks and Pathways, Genome-Wide Association Study
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