The DOI matches its Crossref record; 103 of the 104 references it cites were found in a registry or an index.
Protein networks in disease
Open accessAbstract
During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage.
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
“Human disease genes tend to interact with each other with higher network connectivity than non-disease genes [71].”
From An integrated analysis of genes and functional pathways for aggression in human and rodent models (2019) · cites this paper as [71]
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
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
Network 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
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
The Gene Ontology (GO) database and informatics resource
Gene Ontology Consortium (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh036 · Cited by 4,596
From molecular to modular cell biology.
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632
A comprehensive two-hybrid analysis to explore the yeast protein interactome
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., & Sakaki, Y. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.061034498 · Cited by 3,566
Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders
Hamosh, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki033 · Cited by 3,443
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242
A census of human cancer genes
Futreal, P. A., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., & Stratton, M. R. (2004) · Nature Reviews Cancer · doi:10.1038/nrc1299 · Cited by 3,184
Flagged first, then by citations
1 of the 104 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–200 of 817
Zhang, Z.-C., Zhang, X.-F., Wu, M., Ou-Yang, L., Zhao, X., & Li, X. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa157 · Cited by 80
Graphlet-based edge clustering reveals pathogen-interacting proteins
Solava, R. W., Michaels, R. P., & Milenković, T. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts376 · Cited by 80
Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment
Lensink, M. F., Brysbaert, G., RAOURAOUA, N., Bates, P. A., Giulini, M., Honorato, R. V., van Noort, C. W., Teixeira, J. M. C., Bonvin, A. M. J. J., Kong, R., et al. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26609 · Cited by 86
DOMINO: a network‐based active module identification algorithm with reduced rate of false calls
Levi, H., Elkon, R., & Ron Shamir, A. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209593 · Cited by 76
Tăcutu, R., Budovsky, A., & Fraifeld, V. E. (2010) · Biogerontology · doi:10.1007/s10522-010-9265-8 · Cited by 77
Protein‐protein interactions: Making sense of networks via graph‐theoretic modeling
Pržulj, N. (2010) · BioEssays · doi:10.1002/bies.201000044 · Cited by 78
Omics-Based Molecular Target and Biomarker Identification
Hu, Z.-Z., Huang, H., Wu, C. H., Jung, M., Dritschilo, A., Riegel, A. T., & Wellstein, A. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-027-0_26 · Cited by 74
EGAD: ultra-fast functional analysis of gene networks
Ballouz, S., Weber, M., Pavlidis, P., & Gillis, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw695 · Cited by 74
Systems Biology of Fungal Infection
Horn, F., Heinekamp, T., Kniemeyer, O., Pollmächer, J., Valiante, V., & Brakhage, A. A. (2012) · Frontiers in Microbiology · doi:10.3389/fmicb.2012.00108 · Cited by 76
Measuring rank robustness in scored protein interaction networks
Bozhilova, L. V., Whitmore, A. V., Wray, J., Reinert, G., & Deane, C. M. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3036-6 · Cited by 70
A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies
Akula, N., Baranova, A. V., Seto, D., Solka, J. L., Nalls, M. A., Singleton, A., Ferrucci, L., Tanaka, T., Bandinelli, S., Cho, Y. S., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024220 · Cited by 73
Kumar, S. U., Kumar, D. T., Ramamoorthy, S., Doss, C. G. P., Younes, S. N., Younes, N., Sidenna, M., & Zayed, H. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00276 · Cited by 71
A Generally Applicable Translational Strategy Identifies S100A4 as a Candidate Gene in Allergy
Bruhn, S., Yu, F. H. Z., Barrenäs, F., Gustafsson, M., Zhang, H., Konstantinell, A., Krönke, A., Sönnichsen, B., Bresnick, A. R., Dulyaninova, N. G., et al. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3007410 · Cited by 71
Network‐based drugs and biomarkers
Erler, J. T., & Linding, R. (2009) · The Journal of Pathology · doi:10.1002/path.2646 · Cited by 72
Low duplicability and network fragility of cancer genes
Rambaldi, D., Giorgi, F. M., Capuani, F., Ciliberto, A., & Ciccarelli, F. D. (2008) · Trends in Genetics · doi:10.1016/j.tig.2008.06.003 · Cited by 66
Eronen, L., & Toivonen, H. T. T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-119 · Cited by 70
Iranmanesh, S. M., & Guo, N. L. (2014) · Cancer Informatics · doi:10.4137/cin.s14055 · Cited by 70
Subnetwork State Functions Define Dysregulated Subnetworks in Cancer
Chowdhury, S. A., Nibbe, R. K., Chance, M. R., & Koyutürk, M. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2010.0269 · Cited by 69
Protein-protein interactions: Methods, databases, and applications in virus-host study
Farooq, Q. U. A., Shaukat, Z., Aiman, S., & Li, C. (2021) · World Journal of Virology · doi:10.5501/wjv.v10.i6.288 · Cited by 71
Spreading of diseases through comorbidity networks across life and gender
Chmiel, A., Klimek, P., & Stefan Thurner, A. (2014) · New Journal of Physics · doi:10.1088/1367-2630/16/11/115013 · Cited by 67
Somvanshi, P. R., & Venkatesh, K. V. (2013) · Systems and Synthetic Biology · doi:10.1007/s11693-013-9125-3 · Cited by 70
Chronic disease prediction using administrative data and graph theory: The case of type 2 diabetes
Khan, A. M., Uddin, S., & Srinivasan, U. S. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.048 · Cited by 69
Recent advances in network-based methods for disease gene prediction
Ata, S. K., Wu, M., Fang, Y., LE, O.-Y., Kwoh, C. K., & Li, X. (2021) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 69
Integrated network analysis of transcriptomic and proteomic data in psoriasis
Пирузян, Э. С., Bruskin, S. A., Ishkin, A., Abdeev, R. M., Moshkovskii, S. A., Melnik, S., Nikolsky, Y., & Nikolskaya, T. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-41 · Cited by 67
Studying protein-protein interaction networks: a systems view on diseases
Jordán, F., Nguyen, T.-P., & Liu, W. (2012) · Briefings in Functional Genomics · doi:10.1093/bfgp/els035 · Cited by 71
Odenkirk, M. T., Reif, D. M., & Baker, E. S. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.0c04850 · Cited by 68
Leveraging network pharmacology for drug discovery: Integrative approaches and emerging insights
Li, L., & Kar, S. (2025) · Medicine in Drug Discovery · doi:10.1016/j.medidd.2025.100220 · Cited by 75
Detecting disease genes based on semi-supervised learning and protein–protein interaction networks
Nguyen, T., & Ho, T.-B. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.09.003 · Cited by 69
Prediction of Human Disease-Related Gene Clusters by Clustering Analysis
Sun, P. G., Gao, L., & Han, S. (2011) · International Journal of Biological Sciences · doi:10.7150/ijbs.7.61 · Cited by 63
Global Network Alignment in the Context of Aging
Faisal, F. E., Zhao, H., & Milenković, T. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2326862 · Cited by 63
Network-based Identification of Novel Cancer Genes
Östlund, G., Lindskog, M., & Sonnhammer, E. L. L. (2009) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m900227-mcp200 · Cited by 64
Hallock, P., & Thomas, M. A. (2012) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2011.0054 · Cited by 64
Kim, H., Lee, S., Yoon, J., Song, J., & Park, H. G. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113587 · Cited by 63
Alberghina, L., Mavelli, G., Drovandi, G., Palumbo, P. J., Pessina, S., Tripodi, F., Coccetti, P., & Vanoni, M. (2011) · Biotechnology Advances · doi:10.1016/j.biotechadv.2011.07.010 · Cited by 64
PLK4 is a determinant of temozolomide sensitivity through phosphorylation of IKBKE in glioblastoma
Zhang, Z., Wang, Z., Huang, K., Liu, Y., Wei, C., Zhou, J., Zhang, W., Wang, Q., Liang, H., Zhang, A., et al. (2018) · Cancer Letters · doi:10.1016/j.canlet.2018.11.034 · Cited by 62
Complex biomarker discovery in neuroimaging data: Finding a needle in a haystack
Atluri, G., Padmanabhan, K., Fang, G., Steinbach, M. S., Petrella, J. R., Lim, K. O., MacDonald, A. W., Samatova, N. F., Doraiswamy, P. M., & Kumar, V. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.07.004 · Cited by 61
Structural Bioinformatics of the Interactome
Petrey, D. S., & Honig, B. H. (2014) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-051013-022726 · Cited by 61
Roscoe, B. P., & Bolon, D. N. A. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.05.019 · Cited by 58
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
Bimodal gene expression patterns in breast cancer
Bessarabova, M., Kirillov, E., Shi, W., Bugrim, A. E., Nikolsky, Y., & Nikolskaya, T. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-s1-s8 · Cited by 59
Systems Biology Approaches for Discovering Biomarkers for Traumatic Brain Injury
Feala, J., AbdulHameed, M. D. M., Yu, C., Dutta, B., Yu, X., Schmid, K. E., Dave, J. R., Tortella, F. C., & Jaques Reifman, A. (2013) · Journal of Neurotrauma · doi:10.1089/neu.2012.2631 · Cited by 57
Falter‐Braun, P. (2012) · PROTEOMICS · doi:10.1002/pmic.201100598 · Cited by 58
Li, M., Meng, X., Zheng, R., Wu, F., Li, Y., Pan, Y. F., & Wang, J. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2749571 · Cited by 56
On the performance of de novo pathway enrichment
Batra, R., Alcaraz, N., Gitzhofer, K., Pauling, J. K., Ditzel, H. J., Hellmuth, M., Baumbach, J., & List, M. (2017) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0007-2 · Cited by 58
Revealing Missing Parts of the Interactome via Link Prediction
Hulovatyy, Y., Solava, R. W., & Milenković, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090073 · Cited by 51
Recent approaches to the prioritization of candidate disease genes
Doncheva, N. T., Kacprowski, T., & Albrecht, M. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1177 · Cited by 56
Chen, J., Sam, L. T., Huang, Y., Lee, Y., Li, J., Liu, Y., Xing, H. R., & Lussier, Y. A. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2010.03.009 · Cited by 54
Cierpicki, T., & Grembecka, J. (2015) · Immunological Reviews · doi:10.1111/imr.12244 · Cited by 53
Li, J., Zhao, H., Xuan, Z., Yu, J., Feng, X., Liao, B., & Wang, L. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2934958 · Cited by 53
Network based analysis of hepatitis C virus Core and NS4B protein interactions
Tripathi, L. P., Kataoka, C., Taguwa, S., Moriishi, K., Mori, Y., Matsuura, Y., & Mizuguchi, K. (2010) · Molecular BioSystems · doi:10.1039/c0mb00103a · Cited by 52
Khan, S. H., Ahmad, F., Ahmad, N., Flynn, D. C., & Kumar, R. (2011) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2011.10508619 · Cited by 52
Protein complex prediction for large protein protein interaction networks with the Core&Peel method
Pellegrini, M., Baglioni, M., & Geraci, F. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1191-6 · Cited by 52
TWIST1 and chromatin regulatory proteins interact to guide neural crest cell differentiation
Fan, X., Masamsetti, V. P., Sun, J. Q. J., Engholm‐Keller, K., Osteil, P., Studdert, J. B., Graham, M. E., Fossat, N., & Tam, P. (2021) · eLife · doi:10.7554/elife.62873 · Cited by 53
Network Topology Reveals Key Cardiovascular Disease Genes
Sarajlic, A., Janjic, V., Stojković, N., Radak, D., & Przulj, N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071537 · Cited by 49
Kahle, J. J., Gulbahce, N., Shaw, C. A., Lim, J., HILL, D. E., Barabási, A.-Ĺ., & Zoghbi, H. Y. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq496 · Cited by 51
Alamgir, M. S., Eroukova, V., Jessulat, M., Xu, J., & Golshani, A. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-583 · Cited by 49
PROTEIN INTERACTIONS AND DISEASE
Kann, M. G., Ofran, Y., Punta, M., & Predrag Radivojac, A. (2007) · Cited by 50
Negative feedback confers mutational robustness in yeast transcription factor regulation
Denby, C. M., Im, J. H., Yu, R. C., Pesce, C. G., & Brem, R. B. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116360109 · Cited by 51
Comparative analysis of protein networks
Atias, N., & Sharan, R. (2012) · Communications of the ACM · doi:10.1145/2160718.2160738 · Cited by 50
Self-citationYu, J., & Finley, R. L. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn597 · Cited by 50
Luo, J., & Liang, S. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.11.004 · Cited by 48
Dutta, B., Pusztai, L., Qi, Y., André, F., Lazar, V., Bianchini, G., Ueno, N. T., Agarwal, R., Wang, B., Shiang, C. Y., et al. (2012) · British Journal of Cancer · doi:10.1038/bjc.2011.584 · Cited by 52
Structure-oriented prediction in complex networks
Ren, Z.-M., Zeng, A., & Zhang, Y. (2018) · Physics Reports · doi:10.1016/j.physrep.2018.05.002 · Cited by 48
Boran, A. D. W., & Iyengar, R. (2010) · Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine · doi:10.1002/msj.20191 · Cited by 51
Multiplex gene and phenotype network to characterize shared genetic pathways of epilepsy and autism
Peng, J. J., Zhou, Y., & Wang, K. (2021) · Scientific Reports · doi:10.1038/s41598-020-78654-y · Cited by 47
Biological function through network topology: a survey of the human diseasome
Janjić, V., & Pržulj, N. (2012) · Briefings in Functional Genomics · doi:10.1093/bfgp/els037 · Cited by 50
Tailoring Peptidomimetics for Targeting Protein–Protein Interactions
Akram, O. N., DeGraff, D. J., Sheehan, J. H., Tilley, W. D., Matusik, R. J., Ahn, J., & Raj, G. V. (2014) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-13-0611 · Cited by 47
Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding
Wu, L., Han, L., Li, Q., Wang, G., Zhang, H., & Li, L. (2020) · Molecular Plant · doi:10.1016/j.molp.2020.12.012 · Cited by 49
Villoslada, P., & Baranzini, S. E. (2012) · Journal of Neuroimmunology · doi:10.1016/j.jneuroim.2012.01.001 · Cited by 48
Prioritizing disease candidate genes by a gene interconnectedness-based approach
Hsu, C., Huang, Y., Hsu, C.-T., & Yang, U. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-s3-s25 · Cited by 48
Reverse‐engineering human regulatory networks
Lefèbvre, C., Rieckhof, G. E., & Califano, A. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1159 · Cited by 50
Synaptic GAP and GEF Complexes Cluster Proteins Essential for GTP Signaling
Wilkinson, B., Li, J., & Coba, M. P. (2017) · Scientific Reports · doi:10.1038/s41598-017-05588-3 · Cited by 47
Network integration of multi-tumour omics data suggests novel targeting strategies
do Valle, Í. F., Menichetti, G., Simonetti, G., Bruno, S., Zironi, I., Durso, D. F., Mombach, J. C. M., Martinelli, G., Castellani, G., & Remondini, D. (2018) · Nature Communications · doi:10.1038/s41467-018-06992-7 · Cited by 48
GenRev: Exploring functional relevance of genes in molecular networks
Zheng, S., & Zhao, Z. (2011) · Genomics · doi:10.1016/j.ygeno.2011.12.005 · Cited by 46
Proteome-wide prediction of protein-protein interactions from high-throughput data
Liu, Z.-P., & Chen, L. (2012) · Protein & Cell · doi:10.1007/s13238-012-2945-1 · Cited by 45
Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines
Akan, P., Alexeyenko, A., Costea, P. I., Hedberg, L., Solnestam, B. W., Lundin, S., Hällman, J., Lundberg, E. K., Uhlén, M., & Lundeberg, J. (2012) · Genome Medicine · doi:10.1186/gm387 · Cited by 49
Identifying Responsive Functional Modules from Protein-Protein Interaction Network
Wu, Z., Zhao, X., & Chen, L. (2009) · Molecules and Cells · doi:10.1007/s10059-009-0035-x · Cited by 46
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
A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases
Zolotareva, O., & Kleine, M. (2019) · Journal of Integrative Bioinformatics · doi:10.1515/jib-2018-0069 · Cited by 46
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
Mitochondrial Targets for Pharmacological Intervention in Human Disease
Malty, R. H., Jessulat, M., Jin, K., Musso, G. A., Vlasblom, J., Phanse, S., Zhang, Z., & Babu, M. (2014) · Journal of Proteome Research · doi:10.1021/pr500813f · Cited by 45
Güney, E., Garcı́a-Garcı́a, J., & Oliva, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu092 · Cited by 45
Martínez-Romero, M., Vázquez-Naya, J. M., Rabuñal, J. R., Pita‐Fernández, S., Macenlle, R., Alvariño, J. C., López-Rosés, L., Ulla, J. L., Martinez-Calvo, A. V., Vazquez, S., et al. (2010) · Current Drug Metabolism · doi:10.2174/138920010791514289 · Cited by 45
Network-Based Strategies Can Help Mono- and Poly-pharmacology Drug Discovery: A Systems Biology View
Engin, H., Gürsoy, A., Nussinov, R., & Keskin, Ö. (2014) · Current Pharmaceutical Design · doi:10.2174/13816128113199990066 · Cited by 45
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
Ramly, B., Afiqah‐Aleng, N., & Mohamed‐Hussein, Z. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20122959 · Cited by 45
From protein interaction networks to novel therapeutic strategies
Jaeger, S., & Aloy, P. (2012) · IUBMB Life · doi:10.1002/iub.1040 · Cited by 44
Computational biology for cardiovascular biomarker discovery
Azuaje, F. J., Devaux, Y., & Wagner, D. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp008 · Cited by 44
Dysregulated A to I RNA editing and non-coding RNAs in neurodegeneration
Singh, M. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2012.00326 · Cited by 44
Kelly, M. R., Kostyrko, K., Han, K., Mooney, N. A., Jeng, E. E., Spees, K., Dinh, P., Abbott, K. L., Gwinn, D. M., Sweet-Cordero, A., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1274 · Cited by 43
Janjić, V., & Pržulj, N. (2012) · Molecular BioSystems · doi:10.1039/c2mb25230a · Cited by 44
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
Zhu, J., Qin, Y., Liu, T., Wang, J., & Zheng, X. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s5-s5 · Cited by 42
Saha, S., Sengupta, K., Chatterjee, P., Basu, S., & Nasipuri, M. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx024 · Cited by 42
Zhong, X. Y., Yang, H., Zhao, S., Shyr, Y., & Li, B. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s7-s7 · Cited by 41
Systems biology impact on antiepileptic drug discovery
Margineanu, D. G. (2011) · Epilepsy Research · doi:10.1016/j.eplepsyres.2011.10.006 · Cited by 42
Understanding cancer mechanisms through network dynamics
Cheng, T. M. K., Gulati, S., Agius, R., & Bates, P. A. (2012) · Briefings in Functional Genomics · doi:10.1093/bfgp/els025 · Cited by 39
Shi, W., Bessarabova, M., Dosymbekov, D., Dezső, Z., Nikolskaya, T., Dudoladova, M., Serebryiskaya, T., Bugrim, A. E., Guryanov, A., Brennan, R., et al. (2010) · The Pharmacogenomics Journal · doi:10.1038/tpj.2010.35 · Cited by 41
Glaab, E. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv044 · Cited by 42
Handbook of systems biology : concepts and insights
Walhout, A. J. M., Vidal, M., & Dekker, J. (2013) · The Journal of the American Medical Association (JAMA) Network (American Medical Association) · doi:10.1016/c2010-0-67190-2 · Cited by 41
Metadata
- Authors
- Ideker, T., & Sharan, R.
- Affiliations
- University of California San Diego (US)
- Tel Aviv University (IL)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2008-04
- Volume
- 18
- Issue
- 4
- Pages
- 644-652
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3863981
- OpenAlex
- W2100025044
- Cited by
- 817
- References
- 104
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 21 · 2023: 30 · 2022: 15 · 2021: 33 · 2020: 50 · 2019: 46 · 2018: 40 · 2017: 42 · 2016: 43 · 2015: 64 · 2014: 61 · 2013: 81 · 2012: 77
- Type
- posted content
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Genome Research (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Israel Science Foundation
- National Institutes of Health
- National Institute of General Medical Sciences (GM 070743, R01GM070743)
- National Institute of Environmental Health Sciences (ES14811, R01ES014811)
- 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 › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- protein-protein interaction network, disease-associated genes, network characteristics, molecular basis of disease, protein function prediction, molecular evolution, infectious diseases, personalized medicine
- MeSH terms
- Disease, Fungal Proteins, Humans, Huntington Disease, Phenotype, Yeasts, Gene Expression Regulation, Neoplastic, Case-Control Studies, Genetic Predisposition to Disease, Protein Interaction Mapping, Genetic Diseases, Inborn, Gene Regulatory Networks
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