The DOI matches its Crossref record; 139 of the 140 references it cites were found in a registry or an index.
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.
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?
In the citing paper
“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]
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
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.
Cited by
Sorted by citations
Items 801–900 of 1,013
Multi-level differential network analysis of COPD exacerbations
Noell, G. G., Cosío, B. G., Faner, R., Monsó, E., Peces‐Barba, G., de Diego, A., Esteban, C., Gea, J., Rodriguez‐Roisin, R., García-Núñez, M., et al. (2017) · European Respiratory Journal · doi:10.1183/13993003.00075-2017 · Cited by 46
Constructing Disease Similarity Networks Based on Disease Module Theory
Ni, P., Wang, J., Zhong, P., Li, Y., Wu, F., & Pan, Y. F. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2817624 · Cited by 46
Karimi, M., Castagné, R., Delpierre, C., Albertus, G., Berger, É., Vineis, P., Meena Kumari, B., Kelly‐Irving, M., & Chadeau‐Hyam, M. (2019) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2018-212010 · Cited by 45
Liu, C., Li, H., Wang, K., Zhuang, J., Chu, F., Gao, C., Liu, L., Feng, F., Zhou, C., Zhang, W., et al. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00368 · Cited by 47
Directed Networks as a Novel Way to Describe and Analyze Cardiac Excitation: Directed Graph Mapping
Vandersickel, N., Nieuwenhuyse, E. V., Cleemput, N. V., Goedgebeur, J., Haddad, M. E., Neve, J. D., Demolder, A., Strisciuglio, T., Duytschaever, M. F., & Panfilov, A. V. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.01138 · Cited by 46
Biological Network Approaches and Applications in Rare Disease Studies
Zhang, P., & Itan, Y. (2019) · Genes · doi:10.3390/genes10100797 · Cited by 46
ProphNet: A generic prioritization method through propagation of information
Martínez, V., Cano, C., & Blanco, A. J. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s1-s5 · Cited by 46
Network Pharmacology for Cancer Drug Discovery: Are We There Yet?
Azmi, A. S. (2012) · Future Medicinal Chemistry · doi:10.4155/fmc.12.44 · Cited by 45
IntScore: a web tool for confidence scoring of biological interactions
Kamburov, A., Stelzl, U., & Herwig, R. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks492 · Cited by 45
miREFRWR: a novel disease-related microRNA-environmental factor interactions prediction method
Chen, X. (2015) · Molecular BioSystems · doi:10.1039/c5mb00697j · Cited by 45
Rodina, A., Xu, C., Digwal, C. S., Joshi, S., Patel, Y., Santhaseela, A. R., Bay, S., Merugu, S., Alam, A., Yan, P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39241-7 · Cited by 46
A framework towards digital twins for type 2 diabetes
Zhang, Y., Qin, G., Aguilar, B. H., Rappaport, N., Yurkovich, J. T., Pflieger, L. T., Huang, S., Hood, L., & Shmulevich, I. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1336050 · Cited by 48
Structural Systems Pharmacology: The Role of 3D Structures in Next-Generation Drug Development
Duran‐Frigola, M., Mosca, R., & Aloy, P. (2013) · Chemistry & Biology · doi:10.1016/j.chembiol.2013.03.004 · Cited by 44
Masoudi‐Nejad, A., & Asgari, Y. (2014) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2014.01.007 · Cited by 45
Güney, E., Garcı́a-Garcı́a, J., & Oliva, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu092 · Cited by 45
Genetic Variations and Diseases in UniProtKB/Swiss‐Prot: The Ins and Outs of Expert Manual Curation
Famiglietti, M. L., Estreicher, A., Gos, A., Bolleman, J. T., Géhant, S., Breuza, L., Bridge, A. J., Poux, S., Redaschi, N., Bougueleret, L., et al. (2014) · Human Mutation · doi:10.1002/humu.22594 · Cited by 45
Tan, N. Y. L., Chung, M. K., Smith, J. D., Hsu, J., Serre, D., Newton, D. W., Castel, L., Soltesz, E. G., Pettersson, G., Gillinov, A. M., et al. (2013) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000133 · Cited by 44
Identifying Network-Based Biomarkers of Complex Diseases from High-Throughput Data
Liu, Z.-P. (2016) · Biomarkers in Medicine · doi:10.2217/bmm-2015-0035 · Cited by 46
Wang, J., Guo, Z., Fu, Y., Wu, Z., Huang, C., Zheng, C., Shar, P. A., Wang, Z., Xiao, W., & Wang, Y. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw018 · Cited by 44
Drug development in IBD: from novel target identification to early clinical trials
Danese, S., Fiocchi, C., & Panés, J. (2016) · Gut · doi:10.1136/gutjnl-2016-311717 · Cited by 46
Understanding cancer complexome using networks, spectral graph theory and multilayer framework
Rai, A., Pradhan, P., Nagraj, J., Lohitesh, K., Chowdhury, R., & Jalan, S. (2017) · Scientific Reports · doi:10.1038/srep41676 · Cited by 48
Dissecting the Genetics of Osteoporosis using Systems Approaches
Al-Barghouthi, B. M., & Farber, C. R. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.10.004 · Cited by 47
A comprehensive review of computational techniques for the prediction of drug side effects
Sachdev, K., & Gupta, M. K. (2020) · Drug Development Research · doi:10.1002/ddr.21669 · Cited by 45
Termine, A., Fabrizio, C., Strafella, C., Caputo, V., Petrosini, L., Caltagirone, C., Giardina, E., & Cascella, R. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11040280 · Cited by 47
Data science: a game changer for science and innovation
Grossi, V., Giannotti, F., Pedreschi, D., Manghi, P., Pagano, P., & Assante, M. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-020-00240-2 · Cited by 51
Artificial intelligence for COVID-19: battling the pandemic with computational intelligence
Xu, Z., Su, C., Xiao, Y., & Wang, F. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.09.001 · Cited by 48
Identifying influential spreaders in complex networks by an improved gravity model
Li, Z., & Huang, X. (2021) · Scientific Reports · doi:10.1038/s41598-021-01218-1 · Cited by 45
Xu, J., Wang, P., Yang, H. J., Zhou, J., Li, Y., Li, X., Xue, W., Yu, C., Tian, Y., & Zhu, F. (2016) · BioMed Research International · doi:10.1155/2016/2509385 · Cited by 45
General Framing of Low-, Mid-, and High-Level Data Fusion With Examples in the Life Sciences
Smolinska, A., Engel, J., Szymanska, E., Buydens, L. M. C., & Blanchet, L. (2019) · Data handling in science and technology · doi:10.1016/b978-0-444-63984-4.00003-x · Cited by 49
Jha, P. K., Vijay, A., Halu, A., Uchida, S., & Aikawa, M. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.623012 · Cited by 44
The future of neuromodulation: smart neuromodulation
Ridder, D. D., Maciaczyk, J., & Vanneste, S. (2021) · Expert Review of Medical Devices · doi:10.1080/17434440.2021.1909470 · Cited by 44
Paci, P., Fiscon, G., Conte, F., Wang, R., Handy, D. E., Farina, L., & Loscalzo, J. (2022) · npj Systems Biology and Applications · doi:10.1038/s41540-022-00221-0 · Cited by 49
Self-citationMass Spectrometry–Based Proteomics of Epithelial Ovarian Cancers: A Clinical Perspective
Qian, L., Sun, R., Xue, Z., & Guo, T. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100578 · Cited by 46
Identifying influential nodes in complex networks using a gravity model based on the H-index method
Zhu, S., Zhan, J., & Li, X. (2023) · Scientific Reports · doi:10.1038/s41598-023-43585-x · Cited by 44
Molecular and clinical diseasome of comorbidities in exacerbated COPD patients
Faner, R., Gutiérrez‐Sacristán, A., Castro-Acosta, A. A., Grosdidier, S., Gan, W., Sánchez, M. T. H., López-Campos, J. L., Pozo-Rodrı́guez, F., Sanz, F., Mannino, D. M., et al. (2015) · European Respiratory Journal · doi:10.1183/13993003.00763-2015 · Cited by 44
The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs
Vialaneix, N., Liaubet, L., Laurent, T., Cherel, P., Gamot, A., & SanCristobal, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060045 · Cited by 42
Guo, Y., Wei, X., Das, J., Grimson, A., Lipkin, S. M., Clark, A. G., & Yu, H. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.05.022 · Cited by 44
Rotival, M., & Petretto, E. G. (2013) · Briefings in Functional Genomics · doi:10.1093/bfgp/elt030 · Cited by 41
From protein interaction networks to novel therapeutic strategies
Jaeger, S., & Aloy, P. (2012) · IUBMB Life · doi:10.1002/iub.1040 · Cited by 44
Corradini, B. R., Iamashita, P., Tampellini, E., Farfel, J. M., Grinberg, L. T., & Moreira‐Filho, C. A. (2014) · BioMed Research International · doi:10.1155/2014/543673 · Cited by 41
Computerized techniques pave the way for drug-drug interaction prediction and interpretation
Safdari, R., Ferdousi, R., Aziziheris, K., Kalhori, S. R. N., & Omidi, Y. (2016) · Bioimpacts · doi:10.15171/bi.2016.10 · Cited by 46
Arumughan, A., Roske, Y., Barth, C., Lleras-Forero, L., Bravo‐Rodriguez, K., Redel, A., Kostova, S., McShane, E., Opitz, R. J., Faelber, K., et al. (2016) · Nature Communications · doi:10.1038/ncomms13047 · Cited by 43
Bidkhori, G., Benfeitas, R., Elmas, E., Kararoudi, M. N., Arif, M., Uhlén, M., Nielsen, J., & Mardinoğlu, A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00916 · Cited by 43
Pathway-based classification of genetic diseases
Iourov, I. Y., Vorsanova, S. G., & Yurov, Y. B. (2019) · Molecular Cytogenetics · doi:10.1186/s13039-019-0418-4 · Cited by 44
Heterogeneous graph inference with matrix completion for computational drug repositioning
Yang, M., Huang, L., Xu, Y., Lu, C., & Wang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1024 · Cited by 44
Das, J. K., Tradigo, G., Veltri, P., Guzzi, P. H., & Roy, S. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa420 · Cited by 44
STATegra: Multi-Omics Data Integration – A Conceptual Scheme With a Bioinformatics Pipeline
Planell, N., Lagani, V., Sebastián-León, P., van der Kloet, F. M., Ewing, E., Karathanasis, N., Urdangarin, A., Arozarena, I., Jagodic, M., Tsamardinos, I., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.620453 · Cited by 46
A convergent molecular network underlying autism and congenital heart disease
Rosenthal, S. B., Willsey, H. R., Xu, Y., Yuan, M., Dea, J., Wang, S., Curtis, C., Sempou, E., Khokha, M. K., Chi, N. C., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2021.07.009 · Cited by 41
Roca, J., Vargas, C., Cano, I., Selivanov, V. A., Barreiro, E., Maier, D., Falciani, F., Wagner, P. D., Cascante, M., García‐Aymerich, J., et al. (2014) · Journal of Translational Medicine · doi:10.1186/1479-5876-12-s2-s3 · Cited by 48
Diagnosis – the limiting focus of taxonomy
Sturmberg, J. P., & Martin, C. M. (2014) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.12113 · Cited by 43
Network alignment and motif discovery in dynamic networks
Cinaglia, P., & Cannataro, M. (2022) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-022-00383-1 · Cited by 43
The Precision Toxicology initiative
The PrecisionTox Consortium (2023) · Toxicology Letters · doi:10.1016/j.toxlet.2023.05.004 · Cited by 47
Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks
Hase, T., Ghosh, S., Yamanaka, R., & Kitano, H. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003361 · Cited by 43
MicroRNAs act complementarily to regulate disease-related mRNA modules in human diseases
Chavali, S., Bruhn, S., Tiemann, K., Sætrom, P., Barrenäs, F., Saito, T., Kanduri, K., Wang, H., & Benson, M. (2013) · RNA · doi:10.1261/rna.038414.113 · Cited by 43
Identifying influential nodes based on local dimension
Pu, J., Chen, X., Wei, D., Liu, Q., & Deng, Y. (2014) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/107/10010 · Cited by 43
Phenome-driven disease genetics prediction toward drug discovery
Chen, Y., Li, L., Zhang, G., & Xu, R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv245 · Cited by 45
Strategies Beyond Genome-Wide Association Studies for Atherosclerosis
Maouche, S., & Schunkert, H. (2012) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.111.232652 · Cited by 44
Network analysis of genes and their association with diseases
Kontou, P. I., Pavlopoulou, A., Dimou, N. L., Pavlopoulos, G. A., & Bagos, P. G. (2016) · Gene · doi:10.1016/j.gene.2016.05.044 · Cited by 43
Network and Pathway-Based Analyses of Genes Associated with Parkinson’s Disease
Hu, Y., Pan, Z., Hu, Y. C., Zhang, L., & Wang, J. (2016) · Molecular Neurobiology · doi:10.1007/s12035-016-9998-8 · Cited by 43
Zhang, P., Tao, L., Zeng, X. L., Qin, C., Chen, S., Zhu, F., Yang, S., Li, Z., Chen, W., & Chen, Y. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.10.013 · Cited by 42
Zhang, Q., Yu, H., Qi, J., Tang, D., Chen, X., Wan, J., Li, P., Hu, H., Wang, Y., & Yuanjia, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171628 · Cited by 43
DISNOR: a disease network open resource
Surdo, P. L., Calderone, A., Iannuccelli, M., Licata, L., Peluso, D., Castagnoli, L., Cesareni, G., & Perfetto, L. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx876 · Cited by 43
Mirza, M. U., Vanmeert, M., Froeyen, M., Ali, A., Rafique, S., & Idrees, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-43129-2 · Cited by 44
Liao, S., Han, L., Zheng, X., Wang, X., Zhang, P., Wu, J., Liu, R., Fu, Y.-L., Sun, J., Kang, X., et al. (2019) · British Journal of Pharmacology · doi:10.1111/bph.14714 · Cited by 42
Plastic diagnostics: The remaking of disease and evidence in personalized medicine
Green, S., Carusi, A., & Hoeyer, K. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.05.023 · Cited by 43
DISNET: a framework for extracting phenotypic disease information from public sources
García, G. L., Rodríguez‐González, A., Santamaría, L. P., del Valle, E. P. G., Zanin, M., & Menasalvas, E. (2020) · PeerJ · doi:10.7717/peerj.8580 · Cited by 44
Drug repositioning for psychiatric and neurological disorders through a network medicine approach
Dias, T. L., Schuch, V., Beltrão-Braga, P. C. B., Martins‐de‐Souza, D., Brentani, H., Franco, G. R., & Nakaya, H. I. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0827-5 · Cited by 44
Data analysis methods for defining biomarkers from omics data
Li, C., Gao, Z., Su, B., Xu, G., & Lin, X. (2021) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-021-03813-7 · Cited by 44
Monti, M. M., Fiorentino, J., Milanetti, E., Gosti, G., & Tartaglia, G. G. (2022) · Entropy · doi:10.3390/e24020141 · Cited by 44
He, B., Wang, K., Xiang, J., Bing, P., Tang, M., Tian, G., Guo, C., Xu, M., & Yang, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac405 · Cited by 44
Papachristou, N., Kotronoulas, G., Δικαίος, Ν., Allison, S. J., Eleftherochorinou, H., Rai, T., Kunz, H. R., Barnaghi, P. M., Miaskowski, C. A., & Bamidis, P. D. (2023) · Seminars in Oncology Nursing · doi:10.1016/j.soncn.2023.151433 · Cited by 46
Noncoding RNAs improve the predictive power of network medicine
Gysi, D. M., & Barabási, A.-Ĺ. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2301342120 · Cited by 42
Self-citationGene set correlation enrichment analysis for interpreting and annotating gene expression profiles
Chang, L.-Y., Lee, M.-Z., Wu, Y., Lee, W.-K., Ma, C.-L., Chang, J.-M., Chen, C.-W., Huang, T.-C., Lee, C., Lee, J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1187 · Cited by 42
Network Output Controllability-Based Method for Drug Target Identification
Wu, L., Shen, Y., Li, M., & Wu, F. (2015) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2015.2391175 · Cited by 42
Agustí, A. G. N., Barberà, J. A., Wouters, E. F. �., Peinado, V. I., & Jeffery, P. K. (2013) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201308-1404pp · Cited by 42
Utility of network integrity methods in therapeutic target identification
Peng, Q., & Schork, N. J. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00012 · Cited by 41
Personalized medicine in psychiatry: New technologies and approaches
Silva, J. A. C. E. (2012) · Metabolism · doi:10.1016/j.metabol.2012.08.017 · Cited by 42
Functional Genomics Applied to Cardiovascular Medicine
Cappola, T. P., & Margulies, K. B. (2011) · Circulation · doi:10.1161/circulationaha.111.027300 · Cited by 42
Zhu, J., Qin, Y., Liu, T., Wang, J., & Zheng, X. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s5-s5 · Cited by 42
Burwell, R. G., Dangerfield, P., Moulton, A., Grivas, T. B., & Cheng, J. C. Y. (2013) · Scoliosis · doi:10.1186/1748-7161-8-4 · Cited by 44
Saha, S., Sengupta, K., Chatterjee, P., Basu, S., & Nasipuri, M. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx024 · Cited by 42
Network-based analysis of diagnosis progression patterns using claims data
Jeong, E., Ko, K., Oh, S., & Han, H. W. (2017) · Scientific Reports · doi:10.1038/s41598-017-15647-4 · Cited by 42
Population-dependent Intron Retention and DNA Methylation in Breast Cancer
Kim, D., Shivakumar, M., Han, S., Sinclair, M. S., Lee, Y.-J., Zheng, Y., Olopade, O. I., Kim, D., & Lee, Y. (2018) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-17-0227 · Cited by 42
Liu, C., Wang, K., Zhuang, J., Gao, C., Li, H., Liu, L., Feng, F., Zhou, C., Yao, K., Deng, L., et al. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01171 · Cited by 42
Pan-cancer mapping of differential protein-protein interactions
Gülfidan, G., Turanlı, B., Beklen, H., Sinha, R., & Arğa, K. Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-60127-x · Cited by 42
Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms
Kang, Y., Thieffry, D., & Cantini, L. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.617282 · Cited by 41
Kostić, D. (2016) · Synthese · doi:10.1007/s11229-016-1248-0 · Cited by 48
“Systems medicine” in the view of von Bertalanffy's “organismic biology” and systems theory
Tretter, F. (2019) · Systems Research and Behavioral Science · doi:10.1002/sres.2588 · Cited by 46
Systems Biology and Personalized Medicine: A Network Approach to Human Disease
Loscalzo, J. (2011) · Proceedings of the American Thoracic Society · doi:10.1513/pats.201006-041ms · Cited by 39
Self-citationPerspectives on Systems Biology Applications in Diabetic Kidney Disease
Komorowsky, C. V., Brosius, F. C., Pennathur, S., & Kretzler, M. (2012) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-012-9382-7 · Cited by 40
Community of protein complexes impacts disease association
Wang, Q., Liu, W., Ning, S., Ye, J., Huang, T., Li, Y., Wang, P., Shi, H., & Li, X. (2012) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.74 · Cited by 41
Systems medicine: helping us understand the complexity of disease
Vandamme, D., Fitzmaurice, W., Kholodenko, B. Ν., & Kölch, W. (2013) · QJM · doi:10.1093/qjmed/hct163 · Cited by 42
Next generation of network medicine: interdisciplinary signaling approaches
Korcsmáros, T., Schneider, M. V., & Superti‐Furga, G. (2017) · Integrative Biology · doi:10.1039/c6ib00215c · Cited by 42
Representation of texts as complex networks: a mesoscopic approach
de Arruda, H. F., Silva, F. N., Marinho, V. Q., Amancio, D. R., & Costa, L. D. F. (2017) · Journal of Complex Networks · doi:10.1093/comnet/cnx023 · Cited by 41
In silico identification of potential key regulatory factors in smoking-induced lung cancer
El-aarag, S. A., Mahmoud, A., Hashem, M. H., Elkader, H. A., Hemeida, A. A., & ElHefnawi, M. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0284-z · Cited by 41
Hu, R., & Sun, X. (2017) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(17)30065-1 · Cited by 41
A comparison of mechanistic signaling pathway activity analysis methods
Amadoz, A., Hidalgo, M. R., Çubuk, C., Carbonell‐Caballero, J., & Dopazo, J. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby040 · Cited by 41
Network-based cancer precision medicine: A new emerging paradigm
Tan, A., Huang, H., Zhang, P., & Li, S. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.05.015 · Cited by 43
Li, S., Qian, Y., Xie, R., Li, Y., Zhao, J., Zhang, Z., Huang, R., Tuo, L., Quan, Y., Yu, Z., et al. (2019) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2019.112029 · Cited by 41
Panga, V., Kallor, A. A., Nair, A., Harshan, S., & Raghunathan, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224632 · Cited by 41
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
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
