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Protein-protein interaction networks and biology—what's the connection?
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“Notably, edges are susceptible to ascertainment biases52,214,215 and confounding factors that can induce spurious relationships178 (FIG. 2b).”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [52]
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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
“Despite the best curation efforts, both the in-teractome and the disease gene list remain incomplete( 6 , 11 – 16 )andbiasedtowardmuch-studied disease genes and disease mechanisms ( 32 , 33 ).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
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“Since different methods have different biases, composite scores taking multiple data-types into account perform better (Hakes et al., 2008; Sanchez Claros & Tramontano, 2012).”
BackgroundMethodSystems biology and gene networks in neurodevelopmental and neurodegenerative disorders
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“The genome-wide approaches yield many more interactions, so most databases typically combine both target-focused and genome-wide experiments 52 .”
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“Moreover, NIMS is also relatively robust to the background network, although available networks such as the PPI network are still incomplete and biased (Figure 3c) [54].”
BackgroundPredicting Protein–Protein Interactions from the Molecular to the Proteome Level
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Fundamentals of protein interaction network mapping
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Choose your partners: dimerization in eukaryotic transcription factors
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“In particular, these tools applied to studies of protein–protein interaction (PPI) networks [23,24], gene interaction (GI) networks [25–27], metabolic interaction (MI) networks [28–31] and gene co-expression (Co-Ex) networks [32,33] have extracted valuable biological information from these different views of molecular…”
MethodData completeness—the Achilles heel of drug-target networks
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“For example, for yeast PPIs, which are extensively studied, only about 50% of them are reported [56, 57].”
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Topological properties of protein interaction networks from a structural perspective
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New Regulators of Wnt/β-Catenin Signaling Revealed by Integrative Molecular Screening
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“Conversely, by providing protein connectivity relationships, proteomic screening approaches can generate mechanistic insights but do not reveal the functional impact of specific proteins on the phenotype of interest (7).”
BackgroundModel criticism based on likelihood-free inference, with an application to protein network evolution
Ratmann, O., Andrieu, C., Wiuf, C., & Richardson, S. T. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0807882106 · Cited by 135
“12, 20, and 21), despite being noisy, incomplete, and static descriptions of the real, transient protein network (22).”
BackgroundRevisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks
Agarwal, S., Deane, C. M., Porter, M. A., & Jones, N. S. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000817 · Cited by 144
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A Versatile Vector Toolkit for Functional Analysis of Rice Genes
He, F., Zhang, F., Sun, W., Ning, Y., & Wang, G. (2018) · Rice · doi:10.1186/s12284-018-0220-7 · Cited by 129
“Analysis of protein expression and protein-protein interaction is essential for understanding the molecular and biochemical functions of candidate genes (Hakes et al. 2008).”
BackgroundProtein function prediction – the power of multiplicity
Rentzsch, R., & Orengo, C. A. (2009) · Trends in biotechnology · doi:10.1016/j.tibtech.2009.01.002 · Cited by 126
Apparently low reproducibility of true differential expression discoveries in microarray studies
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Challenges and Rewards of Interaction Proteomics
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AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology
Ciriello, G., Mina, M., Guzzi, P. H., Cannataro, M., & Guerra, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038107 · Cited by 107
Taxonomies of networks from community structure
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Interactome-based approaches to human disease
Caldera, M., Buphamalai, P., Müller, F., & Menche, J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.04.015 · Cited by 104
Active Learning for Computational Chemogenomics
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“In particular, large ligand–target networks have been reported to suffer from a strong target selection bias [43] , which may force models to learn historic target preferences rather than identifying informative ligand–target patterns [44] .”
BackgroundAstragaloside IV acts through multi-scale mechanisms to effectively reduce diabetic nephropathy
Wang, E., Wang, L., Ding, R., Zhai, M., Ge, R., Zhou, P., Wang, T., Fang, H., Wang, J., & Huang, J. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.104831 · Cited by 101
Schaefer, M. H., Serrano, L. G., & Andrade-Navarro, M. A. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00260 · Cited by 96
“Not surprisingly, highly studied proteins are associated with a higher number of known PPI partners (their degree; Hakes et al., 2008).”
BackgroundPartner-Aware Prediction of Interacting Residues in Protein-Protein Complexes from Sequence Data
Ahmad, S., & Mizuguchi, K. (2011) · PLoS ONE · doi:10.1371/journal.pone.0029104 · Cited by 89
“Protein-protein interactions are crucial for almost all aspects of cellular dynamics in living systems [1], and an enormous amount of research has been conducted to reveal, understand and predict protein-protein interactions at species and cellular levels (e.”
BackgroundGraphlet-based measures are suitable for biological network comparison
Hayes, W. B., Sun, K., & Pržulj, N. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts729 · Cited by 90
“It is based on the assumption that the higher the degrees of two nodes, the more likely they are to be neighbours.”
BackgroundMachine learning on protein–protein interaction prediction: models, challenges and trends
Tang, T., Zhang, X., Liu, Y., Peng, H., Zheng, B., Yin, Y., & Zeng, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad076 · Cited by 95
Guarra, F., & Colombo, G. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00513 · Cited by 82
Protein‐protein interactions: Making sense of networks via graph‐theoretic modeling
Pržulj, N. (2010) · BioEssays · doi:10.1002/bies.201000044 · Cited by 78
“…to directly interact), or the ‘‘spoke’’ (the bait protein of the pulled-down complex is assumed to interact with all associated prey proteins, but no other interactions in the complex are assumed) model [24, 91] and the choice of a model has been shown to have an impact on the PIN topology [23].”
BackgroundSom, A., Harder, C., Greber, B., Siatkowski, M., Paudel, Y., Warsow, G., Clemens, H., Schöler, H. R., & Fuellen, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015165 · Cited by 72
“These properties are in close correspondence with the data reported by [42]: Literature-curated networks containing interactions observed in at least one experimental study (LC-1) are expected to have an average node degree of 7.”
ResultMina, M., & Guzzi, P. H. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2318707 · Cited by 67
Miyamoto‐Sato, E., Fujimori, S., Ishizaka, M., Hirai, N., Masuoka, K., Saito, R., Ozawa, Y., Hino, K., Washio, T., Tomita, M., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009289 · Cited by 67
“underlying physical interactions between cellular components [14].”
InfluentialQuantifying Waddington’s epigenetic landscape: a comparison of single-cell potency measures
Shi, J., Teschendorff, A. E., Chen, W., Chen, L., & Li, T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby093 · Cited by 61
Energy intake, metabolic homeostasis, and human health
Pang, G., Xie, J., Chen, Q., & Zhi-he, H. (2014) · Food Science and Human Wellness · doi:10.1016/j.fshw.2015.01.001 · Cited by 64
“Current research in life science is mainly focused on he construction of undirected unweighted networks such as etabolic network [132,133], protein–protein interaction netork [134,135], and protein–immunoglobulin network [136] nd directed unweighted networks such as neural network for imulating and analyzing the devel…”
BackgroundProteomics and phosphoproteomics in precision medicine: applications and challenges
Giudice, G., & Petsalaki, E. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx141 · Cited by 56
Zhang, M., Zhu, C., Jacomy, A., Lu, L. J., & Jegga, A. G. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.05.006 · Cited by 56
Miller, B. W., Lau, G., Grouios, C., Mollica, E., Barrios‐Rodiles, M., Liu, Y., Datti, A., Morris, Q., Wrana, J. L., & Attisano, L. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.72 · Cited by 55
Computational modeling of protein assemblies
Soni, N., & Madhusudhan, M. S. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.04.006 · Cited by 50
Lopes, A., Sacquin‐Mora, S., Dimitrova, V., Laine, É., Ponty, Y., & Carbone, A. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003369 · Cited by 51
“Protein-protein interactions (PPI) are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design [1–4].”
BackgroundThe pangenome of the genus C lostridium
Udaondo, Z., Duque, E., & Ramos, J. L. (2017) · Environmental Microbiology · doi:10.1111/1462-2920.13732 · Cited by 50
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
Unified Alignment of Protein-Protein Interaction Networks
Malod‐Dognin, N., Ban, K., & Pržulj, N. (2017) · Scientific Reports · doi:10.1038/s41598-017-01085-9 · Cited by 44
Wang, Z., Cao, R., Taylor, K. H., Briley, A., Caldwell, C. W., & Cheng, J. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058793 · Cited by 46
“The linear relationship in the log-log plot of nodedegree distribution (Figure 4B) shows that the gene-gene interaction network is very likely a scale-free network, like human social networks, world wide web, protein-protein interaction networks [23–27], and protein domain co-occurrence networks [28], where most nodes…”
BackgroundSaha, S., Sengupta, K., Chatterjee, P., Basu, S., & Nasipuri, M. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx024 · Cited by 42
Alvarez‐Ponce, D., Feyertag, F., & Chakraborty, S. (2017) · Genome Biology and Evolution · doi:10.1093/gbe/evx117 · Cited by 41
“…of a Manually Curated Signal Transduction Network Large-scale interactomic data sets, such as the one used so far, are known to suffer from very high rates of false positives and false negatives (Bader et al. 2004; Deeds et al. 2006; Hakes et al. 2008; Kelly and Stumpf 2012; Alvarez-Ponce 2017).”
BackgroundIntegration, visualization and analysis of human interactome
Pastrello, C., Pasini, E., Kotlyar, M., Otasek, D., Wong, S., Sangrar, W., Rahmati, S., & Jurišica, I. (2014) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2014.01.151 · Cited by 41
Computational Disease Gene Prioritization: An Appraisal
Gill, N., Singh, S. N., & Aseri, T. C. (2014) · Journal of Computational Biology · doi:10.1089/cmb.2013.0158 · Cited by 41
From evidence to inference: Probing the evolution of protein interaction networks
Ratmann, O., Wiuf, C., & Pinney, J. W. (2009) · HFSP journal · doi:10.2976/1.3167215 · Cited by 38
“…Vazquez et al. , 2003; Aloy and Russell, 2004) toward a broader and more subtle appreciation of the underlying biological mechanisms of network evolution (Monica, 2005; Keskin et al. , 2008) and the effects of sampling, bias, and experimental uncertainty on the available data (Hakes et al. , 2008).”
BackgroundMethodInfluentialSelf-citationInteroPORC: automated inference of highly conserved protein interaction networks
Michaut, M., Kerrien, S., Montecchi‐Palazzi, L., Chauvat, F., Cassier‐Chauvat, C., Aude, J.-C., Legrain, P., & Hermjakob, H. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn249 · Cited by 38
Chen, Y. A., Tripathi, L. P., & Mizuguchi, K. (2016) · Database · doi:10.1093/database/baw009 · Cited by 38
“These interactions were further analysed to infer the network topological properties (42, 43).”
BackgroundWang, Z., Zhang, X. C., Le, M. H., Xu, D. H., Stacey, G. S., & Cheng, J. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017906 · Cited by 37
AlignMCL: Comparative analysis of protein interaction networks through Markov clustering
Mina, M., & Guzzi, P. H. (2012) · IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) · doi:10.1109/bibmw.2012.6470300 · Cited by 36
Graph Convolutional Networks Improve the Prediction of Cancer Driver Genes
Schulte-Sasse, R., Budach, S., Hnisz, D., & Marsico, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30493-5_60 · Cited by 35
Kim, J. H., & Jang, C. S. (2020) · Plant Molecular Biology · doi:10.1007/s11103-020-01084-x · Cited by 36
The yeast two-hybrid system and its role in drug discovery
Lentze, N., & Auerbach, D. (2008) · Expert Opinion on Therapeutic Targets · doi:10.1517/14728222.12.4.505 · Cited by 35
“Currently, the best approach is to combine data sets derived from at least two independent methods for analysis but bioinformatics methods are also becoming increasingly important to eliminate false positives from interaction data sets [15-17] .”
BackgroundPhosphotyrosine Signaling Proteins that Drive Oncogenesis Tend to be Highly Interconnected
Koytiger, G., Kaushansky, A., Gordus, A. G., Rush, A. J., Sorger, P. K., & MacBeath, G. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.025858 · Cited by 34
Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity
Raucci, R., Laine, É., & Carbone, A. (2018) · Structure · doi:10.1016/j.str.2018.04.006 · Cited by 32
Computational identification and analysis of protein short linear motifs
Davey, N. E. (2010) · Frontiers in bioscience · doi:10.2741/3647 · Cited by 33
“Ascertainment bias As with all biological data, PPI data often suffer from ascertainment bias and is not of equal quality across the genome (118); data is often more complete and of higher quality for more easily studied proteins (e.”
BackgroundA survey of computational methods in protein–protein interaction networks
Rasti, S., & Vogiatzis, C. (2018) · Annals of Operations Research · doi:10.1007/s10479-018-2956-2 · Cited by 36
Transcriptional Network Architecture of Breast Cancer Molecular Subtypes
de Anda‐Jáuregui, G., Velazquez-Caldelas, T. E., Enríquez, J. E., & Lemus, E. H. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00568 · Cited by 29
“It is well-known that network structure is intimately linked to functionality in biological networks (Kitano, 2002; Albert, 2005; Serrano, 2007; Hakes et al., 2008).”
BackgroundFrom Midbody Protein−Protein Interaction Network Construction to Novel Regulators in Cytokinesis
Chen, T.-C., Lee, S., Hong, T.-M., Shih, J., Lai, J.-M., Chiou, H.-Y., Yang, S., Chan, C., Kao, C.-Y., Yang, P., et al. (2009) · Journal of Proteome Research · doi:10.1021/pr900325f · Cited by 30
Inferring high-confidence human protein-protein interactions
Yu, X., Wallqvist, A., & Jaques Reifman, A. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-79 · Cited by 27
“The most intuitive approach to infer high-confidence PPIs is to score PPIs based on the number of times an interaction has been reported [9-11].”
BackgroundCategorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets
Yu, X., Ivanic, J., Memišević, V., Wallqvist, A., & Jaques Reifman, A. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.012500 · Cited by 27
“However, different explanations to this rule also emerged: (1) hubs play a vital role in maintaining the network connectivity (54); (2) essentialities of proteins come from their interactions, and hubs are more likely to be involved in essential interactions (55); (3) rejecting explanations 1 and 2 above, Zotenko and…”
BackgroundMethodInteractive Proteomics: What Lies Ahead?
Suter, B., Kittanakom, S., & Štagljar, I. (2008) · BioTechniques · doi:10.2144/000112799 · Cited by 27
de Marco, A. (2008) · Microbial Cell Factories · doi:10.1186/1475-2859-7-20 · Cited by 27
Protein–protein interactions: from global to local analyses
Kelly, W. P., & Stumpf, M. P. H. (2008) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2008.06.010 · Cited by 26
“Comparing multiply verified interactions with the whole dataset shows that the characteristics of the structure change [40].”
BackgroundMaking the right connections: biological networks in the light of evolution
Cg, K., & Pinney, J. W. (2009) · BioEssays · doi:10.1002/bies.200900043 · Cited by 27
“(21)”
Self-citationProtein–protein interaction networks studies and importance of 3D structure knowledge
Lu, H., Fornili, A., & Fraternali, F. (2013) · Expert Review of Proteomics · doi:10.1586/14789450.2013.856764 · Cited by 25
“Apart from experiment protocols, many studies also suggested the need of data standardization [70,71] and validation [67,72] .”
MethodImproving molecular machine learning through adaptive subsampling with active learning
Wen, Y., Li, Z., Xiang, Y., & Reker, D. (2023) · Digital Discovery · doi:10.1039/d3dd00037k · Cited by 24
A novel function prediction approach using protein overlap networks
Liang, S., Zheng, D., Standley, D. M., Guo, H., & Zhang, C. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-61 · Cited by 24
“The constructed networks have a high level of false positives [26] and the predicted protein functions are even noisier.”
BackgroundIns and Outs of Systems Biology vis-à-vis Molecular Biology: Continuation or Clear Cut?
Backer, P. D., Waele, D. D., & Speybroeck, L. V. (2009) · Acta Biotheoretica · doi:10.1007/s10441-009-9089-6 · Cited by 25
Schäferling, M., & Nagl, S. (2011) · Methods in Molecular Biology · doi:10.1007/978-1-61779-043-0_19 · Cited by 22
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
Chung, S. S., Pandini, A., Annibale, A., Coolen, A. C. C., Thomas, N. S. B., & Fraternali, F. (2015) · Scientific Reports · doi:10.1038/srep08540 · Cited by 21
Deep representation learning of protein-protein interaction networks for enhanced pattern discovery
Yan, R., Islam, M. T., & Xing, L. (2024) · Science Advances · doi:10.1126/sciadv.adq4324 · Cited by 21
Systems biology and gene networks in Alzheimer’s disease
Wang, Z.-T., Tan, C., Tan, L., & Yu, J. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.11.007 · Cited by 20
“Until recently, PPI data are still generally incomplete and further studies have emphasized the reduction of bias and improvement of tissue specificity (Fessenden, 2017; Hakes et al., 2008; Hart et al., 2006; Huttlin et al., 2017; Kim et al., 2014; Wilhelm et al., 2014).”
Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology
Peterson, G. J., Pressé, S., Peterson, K. S., & Dill, K. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039052 · Cited by 19
“However, it was noted by [71] that disassortativity may simply be an artifact of the yeast two-hybrid technique, and [72] pointed out that this trend is quite different among different yeast datasets, and in some cases is completely reversed, resulting in assortative mixing, where high degree proteins prefer to link t…”
BackgroundGRAPE: graph-regularized protein language modeling unlocks TCR-epitope binding specificity
Fu, X., Li, P., Chen, H., Rong, M., Chen, Y., Cao, D., Yuan, S., & Lü, A. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf522 · Cited by 18
“The physical interaction network between proteins is essential for information transmission and functional coordination in biological systems [21–23].”
Huang, C., Peng, H.-S., & Ng, K. (2015) · BioMed Research International · doi:10.1155/2015/312047 · Cited by 18
“PPI appears when two or more proteins bind together and perform a biological function [5].”
BackgroundAccurate multiple network alignment through context-sensitive random walk
Jeong, H., & Yoon, B.-J. (2015) · BMC Systems Biology · doi:10.1186/1752-0509-9-s1-s7 · Cited by 18
“They are suspected to include a large number of spurious interactions while still missing many potential protein-protein interactions [34,35].”
BackgroundGiresha, A. S., Urs, D., Manjunatha, J. G., Sophiya, P., Supreetha, B. H., Jayarama, S., & Dharmappa, K. K. (2022) · Scientific Reports · doi:10.1038/s41598-022-10950-1 · Cited by 17
Robin, V., Bodein, A., Scott‐Boyer, M., Leclercq, M., Périn, O., & Droit, A. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.962799 · Cited by 19
Rigid-Docking Approaches to Explore Protein–Protein Interaction Space
Matsuzaki, Y., Uchikoga, N., Ohue, M., & Akiyama, Y. (2016) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2016_41 · Cited by 18
“A number of studies reported assessments of model organism PPIs such as yeast and humans [9] [10].”
BackgroundGTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity
Jiang, F., Guo, Y., Ma, H., Na, S., Zhong, W., Han, Y., Wang, T., & Huang, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae343 · Cited by 17
“Networks formed by protein interactions are crucial as they depict the cellular framework and signal pathways while also influencing signal transmission within cells [22, 23].”
BackgroundEmbryonic stem cells: protein interaction networks
Ng, P. M. L., & Lufkin, T. (2011) · BioMolecular Concepts · doi:10.1515/bmc.2011.008 · Cited by 15
“The observation that essential proteins tend to be more highly connected than nonessential proteins could also be a true property or a consequence of them having been more thoroughly studied, or a combination of the two (56).”
BackgroundINTerface Builder: A Fast Protein–Protein Interface Reconstruction Tool
Dequeker, C., Laine, É., & Carbone, A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00360 · Cited by 15
Predicting novel genes and pathways associated with osteosarcoma by using bioinformatics analysis
Dong, B., Wang, G., Yao, J., Yuan, P., Kang, W., Zhi, L., & He, X. (2017) · Gene · doi:10.1016/j.gene.2017.06.058 · Cited by 14
Xing, C., & Dunson, D. B. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002110 · Cited by 14
“A larger number of predicted PPIs may suggest that the previous estimations may not only have a large false positive (FP) rate [1], but also may have a large false negative (FN) rate.”
InfluentialVaiman, D., & Miralles, F. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165849 · Cited by 14
Saikia, S., Bordoloi, M., Sarmah, R., & Kolita, B. (2018) · Drug Development Research · doi:10.1002/ddr.21475 · Cited by 14
Azuaje, F. J., Devaux, Y., Vausort, M., Yvorra, C., & Wagner, D. R. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2010.05.012 · Cited by 17
“[29].”
Rodríguez-Llorente, I. D., Caviedes, M. Á., Dary, M., Palomares, A. J., Cánovas, F. M., & Peregrín-Alvarez, J. M. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-63 · Cited by 15
“These methods have the advantage over experimental methods and other computational methods based on protein conservation such as Interologs [22] or literature mining [23], that they are not biased towards well studied or conserved proteins or interactions [24].”
MethodAlgorithms to Detect Multiprotein Modularity Conserved during Evolution
Hodgkinson, L. M., & Karp, R. M. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.125 · Cited by 12
“Given incomplete and sometimes unreliable interactomics data [7], Produles attempts to find regions of the interactomes that are reliable by ignoring those regions that do not exhibit modularity in both interactomes.”
BackgroundInfluentialLinkers of Cell Polarity and Cell Cycle Regulation in the Fission Yeast Protein Interaction Network
Vaggi, F., Dodgson, J., Bajpai, A., Chessel, A., Jordán, F., Sato, M., Salas, R. E. C., & Csikász‐Nagy, A. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002732 · Cited by 13
“Furthermore, while we have shown that the ranking of proteins within the communities is robust to noise, the actual communities detected by various algorithms as well as the structure of the network are strongly influenced by the granularity and quality of the interaction data used (Text S4 and [95]).”
Malhotra, A., Younesi, E., Sahadevan, S., Zimmermann, J., & Hofmann‐Apitius, M. (2015) · Scientific Reports · doi:10.1038/srep13634 · Cited by 13
Castrillo, J. I., Pir, P., & Oliver, S. G. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-385944-0.00018-6 · Cited by 14
Proteomics appending a complementary dimension to precision oncotherapy
Zhou, Z., Zhang, R., Zhou, A., Lv, J., Chen, S., Zou, H., Zhang, G., Lin, T., Wang, Z., Zhang, Y., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.044 · Cited by 13
“Never-theless, these algorithms gravitated towards available interactome data and annotation pathway data, as these were presently fragmented and skewed toward heavily expressing proteins[91 – 93].”
Metadata
- Authors
- Hakes, L., Pinney, J. W., Robertson, D. L., & Lovell, S. C.
- Affiliations
- University of Manchester (GB)
- Venue
- Nature Biotechnology
- Published
- 2008-01
- Volume
- 26
- Issue
- 1
- Pages
- 69-72
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2165160727
- Cited by
- 200
- References
- 37
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 7 · 2023: 12 · 2022: 5 · 2021: 3 · 2020: 3 · 2019: 4 · 2018: 9 · 2017: 15 · 2016: 13 · 2015: 17 · 2014: 10 · 2013: 17 · 2012: 16
- FWCI
- 7.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
- Funders
- Wellcome Trust
- Biotechnology and Biological Sciences Research Council (BB/F007620/1, BB/C515412/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- protein-protein interaction network, network analysis
- MeSH terms
- Algorithms, Binding Sites, Biology, Protein Binding, Sensitivity and Specificity, Signal Transduction, Information Storage and Retrieval, Artifacts, Proteome, Protein Interaction Mapping
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