Integrating physical and genetic maps: from genomes to interaction networks
Open accessAbstract
Physical and genetic mapping data have become as important to network biology as they once were to the Human Genome Project. Integrating physical and genetic networks currently faces several challenges: increasing the coverage of each type of network; establishing methods to assemble individual interaction measurements into contiguous pathway models; and annotating these pathways with detailed functional information. A particular challenge involves reconciling the wide variety of interaction types that are currently available. For this purpose, recent studies have sought to classify genetic and physical interactions along several complementary dimensions, such as ordered versus unordered, alleviating versus aggravating, and first versus second degree.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
PPI data sets are biased to highly studied gene categories and are generally incomplete.
“Similar to pathway databases, these PPI data sets are biased to highly studied gene categories (for example, those implicated in cancer biology) and are still generally incomplete2,53 (TABLE 1).”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [2]
Cites
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 12,617 · see citing works
Base-Calling of Automated Sequencer Traces Using Phred. I. Accuracy AssessmentPreprint
Ewing, B., Hillier, L., Wendl, M. C., & Green, P. (1998) · Genome Research · doi:10.1101/gr.8.3.175 · Cited by 6,993 · see citing works
An automated method for finding molecular complexes in large protein interaction networks
Bader, G. D., & Hogue, C. W. V. (2003) · BMC Bioinformatics · doi:10.1186/1471-2105-4-2 · Cited by 6,112 · see citing works
Functional organization of the yeast proteome by systematic analysis of protein complexes
Gavin, A., Böesche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J. M., Michon, A., Cruciat, C., et al. (2002) · Nature · doi:10.1038/415141a · Cited by 4,729 · see citing works
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 · see citing works
Capturing Chromosome Conformation
Dekker, J., Rippe, K., Dekker, M., & Kleckner, N. (2002) · Science · doi:10.1126/science.1067799 · Cited by 3,652 · see citing works
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 · see citing works
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A., et al. (2006) · Nature · doi:10.1038/nature04670 · Cited by 2,915 · see citing works
Towards a proteome-scale map of the human protein–protein interaction network
Rual, J.-F. M., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D., Dreze, M., Ayivi-Guedehoussou, N., et al. (2005) · Nature · doi:10.1038/nature04209 · Cited by 2,904 · see citing works
Proteome survey reveals modularity of the yeast cell machinery
Gavin, A., Aloy, P., Grandi, P., Krause, R., Böesche, M., Marzioch, M., Rau, C., Jensen, L. J., Bastuck, S., Dümpelfeld, B., et al. (2006) · Nature · doi:10.1038/nature04532 · Cited by 2,544 · see citing works
Functional Discovery via a Compendium of Expression Profiles
Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R. B., Armour, C. D., Bennett, H. A., Coffey, E. M., Dai, H. A., He, Y. D., et al. (2000) · Cell · doi:10.1016/s0092-8674(00)00015-5 · Cited by 2,534 · see citing works
The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells
Loh, Y., Wu, Q., Chew, J.-L., Vega, V. B., Zhang, W., Chen, X., Bourque, G., George, J., Leong, B., Liu, J., et al. (2006) · Nature Genetics · doi:10.1038/ng1760 · Cited by 2,438 · see citing works
A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome
Stelzl, U., Worm, U., Lalowski, M. M., Haenig, C., Brembeck, F. H., Goehler, H., Strödicke, M., Zenkner, M., Schoenherr, A., Koeppen, S., et al. (2005) · Cell · doi:10.1016/j.cell.2005.08.029 · Cited by 2,389 · see citing works
Comparative assessment of large-scale data sets of protein–protein interactions
von Mering, C., Krause, R., Snel, ., Cornell, M. J., Oliver, S. G., Fields, S., & Peer Bork, B. (2002) · Nature · doi:10.1038/nature750 · Cited by 2,339 · see citing works
A Protein Interaction Map of Drosophila melanogaster
Giot, L., Bader, J. S., Brouwer, C., Chaudhuri, A., Kuang, B., Li, Y., Hao, Y. L., Ooi, C., Godwin, B., Vitols, E., et al. (2003) · Science · doi:10.1126/science.1090289 · Cited by 2,266 · see citing works
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,235 · see citing works
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl999 · Cited by 2,145 · see citing works
Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants
Tong, A. H. Y., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Pagé, N., Robinson, M., Raghibizadeh, S., Hogue, C. W. V., Bussey, H., et al. (2001) · Science · doi:10.1126/science.1065810 · Cited by 2,138 · see citing works
Transcriptional regulatory code of a eukaryotic genome
Harbison, C. T., Gordon, D. B., Lee, T. I., Rinaldi, N. J., MacIsaac, K. D., Danford, T., Hannett, N. M., Tagne, J., Reynolds, D. B., Yoo, J. Y., et al. (2004) · Nature · doi:10.1038/nature02800 · Cited by 2,116 · see citing works
Global Mapping of the Yeast Genetic Interaction Network
Tong, A. H. Y., Lesage, G., Bader, G. D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G. F., Brost, R. L., Chang, M., et al. (2004) · Science · doi:10.1126/science.1091317 · Cited by 2,070 · see citing works
Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network
Ideker, T., Thorsson, V., Ranish, J. A., Christmas, R., Buhler, J., Eng, J. K., Bumgarner, R., Goodlett, D. R., Aebersold, R., & Hood, L. (2001) · Science · doi:10.1126/science.292.5518.929 · Cited by 2,050 · see citing works
Self-citationGenome-Wide Location and Function of DNA Binding Proteins
Ren, B., Robert, F., Wyrick, J. J., Aparicio, O. M., Jennings, E. G., Simon, I., Zeitlinger, J., Schreiber, J., Hannett, N. M., Kanin, E. I., et al. (2000) · Science · doi:10.1126/science.290.5500.2306 · Cited by 1,768 · see citing works
Analysis of Human Genetic Linkage.
Edwards, A. W. F., & Ott, J. (1992) · Biometrics · doi:10.2307/2532370 · Cited by 1,691 · see citing works
A Map of the Interactome Network of the Metazoan C. elegans
Li, S., Armstrong, C. M., Bertin, N., Ge, H., Milstein, S., Boxem, M., Vidalain, P., Han, J.-D. J., Chesneau, A., Hao, T., et al. (2004) · Science · doi:10.1126/science.1091403 · Cited by 1,628 · see citing works
Genetics of gene expression surveyed in maize, mouse and man
Schadt, E., Monks, S. A., Drake, T., Lusis, A., Che, N., Colinayo, V. V., Ruff, T., Milligan, S. B., Lamb, J., Cavet, G., et al. (2003) · Nature · doi:10.1038/nature01434 · Cited by 1,594 · see citing works
Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles
Pellegrini, M. A., Marcotte, E. M., Thompson, M. J., Eisenberg, D. S., & Yeates, T. O. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.8.4285 · Cited by 1,568 · see citing works
Genome-wide Map of Nucleosome Acetylation and Methylation in Yeast
Pokholok, D., Harbison, C. T., Levine, S. S., Cole, M. F., Hannett, N. M., Lee, T. I., Bell, G. W., Walker, K., Rolfe, A., Herbolsheimer, E., et al. (2005) · Cell · doi:10.1016/j.cell.2005.06.026 · Cited by 1,433 · see citing works
The c-fos protein interacts with c-JunAP-1 to stimulate transcription of AP-1 responsive genes
Chiu, R., Boyle, W. J., Meek, J., Smeal, T., Hunter, T. R., & Karin, M. (1988) · Cell · doi:10.1016/0092-8674(88)90076-1 · Cited by 1,336 · see citing works
Dostie, J., Richmond, T., Arnaout, R., Selzer, R. R., Lee, W. L., Honan, T., Rubio, E. D., Krumm, A., Lamb, J., Nusbaum, C., et al. (2006) · Genome Research · doi:10.1101/gr.5571506 · Cited by 1,182 · see citing works
A Global Map of p53 Transcription-Factor Binding Sites in the Human Genome
Wei, C.-L., Wu, Q., Vega, V. B., Chiu, K. P., Ng, P., Zhang, T., Shahab, A., Yong, H. C., Fu, Y., Weng, Z., et al. (2006) · Cell · doi:10.1016/j.cell.2005.10.043 · Cited by 1,141 · see citing works
Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF
Iyer, V. R., Horak, C. E., Scafe, C., Botstein, D., Snyder, M. P., & Brown, P. O. (2001) · Nature · doi:10.1038/35054095 · Cited by 1,084 · see citing works
Complexes of sequential metabolic enzymes
Srere, P. A. (1987) · Annual Review of Biochemistry · doi:10.1146/annurev.bi.56.070187.000513 · Cited by 1,058 · see citing works
An integrative genomics approach to infer causal associations between gene expression and disease
Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., GuhaThakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., et al. (2005) · Nature Genetics · doi:10.1038/ng1589 · Cited by 1,043 · see citing works
A human phenome-interactome network of protein complexes implicated in genetic disorders
Lage, K., Karlberg, E., Størling, Z. M., Olason, P., Pedersen, A., Rigina, O., Hinsby, A., Tümer, Z., Pociot, F., Tommerup, N., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1295 · Cited by 973 · see citing works
Global analysis of protein phosphorylation in yeast
Ptacek, J., Devgan, G., Michaud, G. A., Zhu, H., Zhu, X., Fasolo, J., Guo, H., Jona, G., Breitkreutz, A., Sopko, R., et al. (2005) · Nature · doi:10.1038/nature04187 · Cited by 970 · see citing works
Schuldiner, M., Collins, S. R., Thompson, N. J., Denic, V., Bhamidipati, A., Punna, T., Ihmels, J., Andrews, B., Boone, C., Greenblatt, J. F., et al. (2005) · Cell · doi:10.1016/j.cell.2005.08.031 · Cited by 870 · cited 1× · see citing works
“Conversely, alleviating interactions have been thought to indicate genes within the same pathwa”
BackgroundCollins, S. R., Miller, K. M., Maas, N. L., Roguev, A., Fillingham, J., Chu, C. S., Schuldiner, M., Gebbia, M., Recht, J., Shales, M., et al. (2007) · Nature · doi:10.1038/nature05649 · Cited by 869 · see citing works
Zhao, R., Davey, M., Hsu, Y.-C., Kaplanek, P., Tong, A., Parsons, A. B., Krogan, N., Cagney, G., Mai, D., Greenblatt, J., et al. (2005) · Cell · doi:10.1016/j.cell.2004.12.024 · Cited by 819 · see citing works
Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae
Collins, S. R., Kemmeren, P., Zhao, X.-C., Greenblatt, J. F., Spencer, F., Holstege, F. C. P., Weissman, J. S., & Krogan, N. J. (2007) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m600381-mcp200 · Cited by 795 · see citing works
Sturtevant, A. H. (1913) · Journal of Experimental Zoology · doi:10.1002/jez.1400140104 · Cited by 766 · see citing works
Analysis of Human Genetic Linkage
Ott, J. (1999) · doi:10.56021/9780801861406 · Cited by 754 · see citing works
High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells
Barrios‐Rodiles, M., Brown, K. R., Ozdamar, B., Bose, R., Liu, Z., Donovan, R. S., Shinjo, F., Liu, Y., Dembowy, J., Taylor, I. W., et al. (2005) · Science · doi:10.1126/science.1105776 · Cited by 726 · see citing works
Berger, M. F., Philippakis, A., Qureshi, A. M., He, F. S., Estep, P. W., & Bulyk, M. L. (2006) · Nature Biotechnology · doi:10.1038/nbt1246 · Cited by 685 · see citing works
A Probabilistic Functional Network of Yeast Genes
Lee, I., Date, S. V., Adai, A. T., & Marcotte, E. M. (2004) · Science · doi:10.1126/science.1099511 · Cited by 682 · see citing works
Exploring genetic interactions and networks with yeast
Boone, C., Bussey, H., & Andrews, B. J. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2085 · Cited by 661 · see citing works
Identification of in vivo DNA targets of chromatin proteins using tethered Dam methyltransferase
van Steensel, B., & Henikoff, S. (2000) · Nature Biotechnology · doi:10.1038/74487 · Cited by 629 · see citing works
Cox, D., Burmeister, M., Price, E. R., Kim, S., & Myers, R. (1990) · Science · doi:10.1126/science.2218528 · Cited by 618 · see citing works
Keene, J. D., Komisarow, J. M., & Friedersdorf, M. B. (2006) · Nature Protocols · doi:10.1038/nprot.2006.47 · Cited by 612 · see citing works
Modular epistasis in yeast metabolism
Segrè, D., DeLuna, A., Church, G. M., & Kishony, R. (2005) · Nature Genetics · doi:10.1038/ng1489 · Cited by 575 · see citing works
Modeling cellular machinery through biological network comparison
Sharan, R., & Ideker, T. E. (2006) · Nature Biotechnology · doi:10.1038/nbt1196 · Cited by 553 · see citing works
Self-citationSethupathy, P., Megraw, M. S., & Hatzigeorgiou, A. G. (2006) · Nature Methods · doi:10.1038/nmeth954 · Cited by 541 · see citing works
A DNA Integrity Network in the Yeast Saccharomyces cerevisiae
Pan, X., Ye, P., Yuan, D., Wang, X., Bader, J. S., & Boeke, J. D. (2006) · Cell · doi:10.1016/j.cell.2005.12.036 · Cited by 538 · see citing works
Correlation of Two-Hybrid Affinity Data with In Vitro Measurements
Estojak, J., Brent, R., & Golemis, E. A. (1995) · Molecular and Cellular Biology · doi:10.1128/mcb.15.10.5820 · Cited by 537 · see citing works
Chemogenomic profiling: Identifying the functional interactions of small molecules in yeast
Giaever, G. N., Flaherty, P., Kumm, J., Proctor, M. J., Nislow, C., Jaramillo, D. F., Chu, A. M., Jordan, M. I., Arkin, A. P., & Davis, R. W. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307490100 · Cited by 506 · see citing works
Lehner, B., Crombie, C., Tischler, J., Fortunato, A., & Fraser, A. G. (2006) · Nature Genetics · doi:10.1038/ng1844 · Cited by 502 · see citing works
Integrative model of the response of yeast to osmotic shock
Klipp, E., Nordlander, B., Krüger, R., Gennemark, P., & Hohmann, S. (2005) · Nature Biotechnology · doi:10.1038/nbt1114 · Cited by 447 · see citing works
Systematic interpretation of genetic interactions using protein networks
Kelley, R., & Ideker, T. (2005) · Nature Biotechnology · doi:10.1038/nbt1096 · Cited by 437 · see citing works
Self-citationGenome annotation: from sequence to biology
Stein, L. D. (2001) · Nature Reviews Genetics · doi:10.1038/35080529 · Cited by 434 · see citing works
Genetic reconstruction of a functional transcriptional regulatory network
Hu, Z., Killion, P. J., & Iyer, V. R. (2007) · Nature Genetics · doi:10.1038/ng2012 · Cited by 415 · see citing works
Probabilistic model of the human protein-protein interaction network
Rhodes, D. R., Tomlins, S. A., Varambally, S., Mahavisno, V., Barrette, T. R., Sundaram, S. K., Ghosh, D., Pandey, A., & Chinnaiyan, A. M. (2005) · Nature Biotechnology · doi:10.1038/nbt1103 · Cited by 411 · see citing works
Assessing experimentally derived interactions in a small world
Goldberg, D. S., & Roth, F. P. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0735871100 · Cited by 401 · see citing works
New method for mapping genes in human chromosomes
Goss, S. J., & Harris, H. (1975) · Nature · doi:10.1038/255680a0 · Cited by 356 · see citing works
Protein interaction networks from yeast to human
Bork, P., Jensen, L. J., von Mering, C., Ramani, A. K., Lee, I., & Marcotte, E. M. (2004) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2004.05.003 · Cited by 344 · see citing works
Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions
St.Onge, R. P., Mani, R., Oh, J., Proctor, M. J., Fung, E., Davis, R. W., Nislow, C., Roth, F. P., & Giaever, G. N. (2007) · Nature Genetics · doi:10.1038/ng1948 · Cited by 306 · see citing works
A strategy for extracting and analyzing large-scale quantitative epistatic interaction data
Collins, S. R., Schuldiner, M., Krogan, N. J., & Weissman, J. S. (2006) · Genome biology · doi:10.1186/gb-2006-7-7-r63 · Cited by 300 · see citing works
Genetic interactions between polymorphisms that affect gene expression in yeast
Brem, R. B., Storey, J. D., Whittle, J., & Kruglyak, L. (2005) · Nature · doi:10.1038/nature03865 · Cited by 298 · see citing works
The Language of Gene Interaction
Phillips, P. C. (1998) · Genetics · doi:10.1093/genetics/149.3.1167 · Cited by 275 · see citing works
Comparison of human genetic and sequence-based physical maps
Yu, A., Zhao, C., Fan, Y., Jang, W., Mungall, A. J. J., Deloukas, P., Olsen, A. S., Doggett, N. A., Ghebranious, N., Broman, K. W., et al. (2001) · Nature · doi:10.1038/35057185 · Cited by 273 · see citing works
Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis
Gunsalus, K. C., Ge, H., Schetter, A. J., Goldberg, D. S., Han, J.-D. J., Hao, T., Berriz, G. F., Bertin, N., Huang, J. I., Chuang, L.-S. F., et al. (2005) · Nature · doi:10.1038/nature03876 · Cited by 269 · see citing works
A Systems Approach to Mapping DNA Damage Response Pathways
Workman, C. T., Mak, H. C., McCuine, S., Tagne, J., Agarwal, M., Ozier, O. W., Begley, T. J., Samson, L. D., & Ideker, T. E. (2006) · Science · doi:10.1126/science.1122088 · Cited by 268 · see citing works
Self-citationOrdering gene function: the interpretation of epistasis in regulatory hierarchies
Avery, L., & Wasserman, S. A. (1992) · Trends in Genetics · doi:10.1016/0168-9525(92)90263-4 · Cited by 247 · see citing works
A Gene-Centered C. elegans Protein-DNA Interaction Network
Deplancke, B., Mukhopadhyay, A., Ao, W., Elewa, A., Grove, C. A., Martinez, N. J., Sequerra, R., Doucette‐Stamm, L., Reece-Hoyes, J. S., Hope, I. A., et al. (2006) · Cell · doi:10.1016/j.cell.2006.04.038 · Cited by 240 · see citing works
Predicting interactions in protein networks by completing defective cliques
Yu, H., Paccanaro, A., Trifonov, V., & Gerstein, M. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl014 · Cited by 223 · see citing works
A Gateway-Compatible Yeast One-Hybrid SystemPreprint
Deplancke, B., Dupuy, D., Vidal, M., & Walhout, A. J. M. (2004) · Genome Research · doi:10.1101/gr.2445504 · Cited by 216 · see citing works
Heritability and Tissue Specificity of Expression Quantitative Trait Loci
Petretto, E. G., Mangion, J., Dickens, N. J., Cook, S. A., Kumaran, M. K., Han, L., Fischer, J., Maatz, H., Křen, V., Pravenec, M., et al. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020172 · Cited by 214 · see citing works
Finding function: evaluation methods for functional genomic data
Myers, C. L., Barrett, D. R., Hibbs, M., Huttenhower, C., & Troyanskaya, O. G. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-187 · Cited by 211 · see citing works
Protein–protein interaction maps: a lead towards cellular functions
Legrain, P., Wojcik, J., & Gauthier, J. (2001) · Trends in Genetics · doi:10.1016/s0168-9525(01)02323-x · Cited by 195 · see citing works
DNA helicase gene interaction network defined using synthetic lethality analyzed by microarray
Ooi, S. L., Shoemaker, D. D., & Boeke, J. D. (2003) · Nature Genetics · doi:10.1038/ng1258 · Cited by 194 · see citing works
Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiaeinteraction network
Zhang, L. V., King, O. D., Wong, S. L., Goldberg, D. S., Tong, A. H., Lesage, G., Andrews, B., Bussey, H., Boone, C. M., & Roth, F. P. (2005) · Journal of Biology · doi:10.1186/jbiol23 · Cited by 190 · see citing works
Studying complex biological systems using multifactorial perturbation
Jansen, R. C. (2003) · Nature Reviews Genetics · doi:10.1038/nrg996 · Cited by 187 · see citing works
WebQTL: rapid exploratory analysis of gene expression and genetic networks for brain and behavior
Chesler, E. J., Lu, L., Wang, J., Williams, R. W., & Manly, K. F. (2004) · Nature Neuroscience · doi:10.1038/nn0504-485 · Cited by 174 · see citing works
High‐throughput two‐hybrid analysis
Fields, S. (2005) · FEBS Journal · doi:10.1111/j.1742-4658.2005.04973.x · Cited by 172 · see citing works
Yeang, C., Ideker, T. E., & Jaakkola, T. (2004) · Journal of Computational Biology · doi:10.1089/1066527041410382 · Cited by 167 · see citing works
Self-citationMathematical modeling of intracellular signaling pathways
Klipp, E., & Liebermeister, W. (2006) · BMC Neuroscience · doi:10.1186/1471-2202-7-s1-s10 · Cited by 158 · see citing works
Unraveling transcription regulatory networks by protein–DNA and protein–protein interaction mappingPreprint
Walhout, A. J. M. (2006) · Genome Research · doi:10.1101/gr.5321506 · Cited by 156 · see citing works
Tewari, M., Hu, P. J., Ahn, J. S., Ayivi-Guedehoussou, N., Vidalain, P., Li, S., Milstein, S., Armstrong, C. M., Boxem, M., Butler, M. D., et al. (2004) · Molecular Cell · doi:10.1016/s1097-2765(04)00033-4 · Cited by 137 · see citing works
Rowen, L., Mahairas, G. G., & Hood, L. (1997) · Science · doi:10.1126/science.278.5338.605 · Cited by 136 · see citing works
Lehner, B., Tischler, J., & Fraser, A. G. (2006) · Nature Protocols · doi:10.1038/nprot.2006.245 · Cited by 136 · see citing works
A thematic review series: systems biology approaches to metabolic and cardiovascular disorders
Lusis, A. J. (2006) · Journal of Lipid Research · doi:10.1194/jlr.e600004-jlr200 · Cited by 135 · see citing works
Gene function prediction from congruent synthetic lethal interactions in yeast
Ye, P., Peyser, B. D., Pan, X., Boeke, J. D., Spencer, F. A., & Bader, J. S. (2005) · Molecular Systems Biology · doi:10.1038/msb4100034 · Cited by 133 · see citing works
Derivation of genetic interaction networks from quantitative phenotype data
Drees, B., Thórsson, V., Carter, G. W., Rives, A. W., Raymond, M. Z., Avila-Campillo, I., Shannon, P. T., & Galitski, T. (2005) · Genome biology · doi:10.1186/gb-2005-6-4-r38 · Cited by 122 · see citing works
Petri Net representations in systems biology
Pinney, J. W., Westhead, D. R., & McConkey, G. A. (2003) · Biochemical Society Transactions · doi:10.1042/bst0311513 · Cited by 120 · see citing works
A direct comparison of protein interaction confidence assignment schemes
Suthram, S., Shlomi, T., Ruppin, E., Sharan, R., & Ideker, T. E. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-360 · Cited by 117 · see citing works
Self-citationProtein–Protein Interaction Panel Using Mouse Full-Length cDNAsPreprint
Suzuki, H., Fukunishi, Y., Kagawa, I., Saito, R., Oda, H., Endo, T., Kondo, S., Bono, H. U., Okazaki, Y., & Hayashizaki, Y. (2001) · Genome Research · doi:10.1101/gr.180101 · Cited by 109 · see citing works
The cell-bag of enzymes or network of channels?
Mathews, C. K. (1993) · Journal of Bacteriology · doi:10.1128/jb.175.20.6377-6381.1993 · Cited by 98 · see citing works
Schadt, E. E., & Lum, P. Y. (2006) · Journal of Lipid Research · doi:10.1194/jlr.r600026-jlr200 · Cited by 98 · see citing works
Ulitsky, I., & Ron Shamir, A. (2007) · Molecular Systems Biology · doi:10.1038/msb4100144 · Cited by 98 · see citing works
Vermeirssen, V., Barrasa, M. I., Hidalgo, C. A., Babon, J. A. B., Sequerra, R., Doucette‐Stamm, L., Barabási, A.-Ĺ., & Walhout, A. J. M. (2007) · Genome Research · doi:10.1101/gr.6148107 · Cited by 96 · see citing works
An integrative approach for causal gene identification and gene regulatory pathway inference
Tu, Z., Wang, L., Arbeitman, M. N., Chen, T., & Sun, F. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl234 · Cited by 94 · see citing works
Integrated Assessment and Prediction of Transcription Factor Binding
Beyer, A., Workman, C. T., Hollunder, J., Radke, D., Möller, U., Wilhelm, T. E., & Ideker, T. E. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020070 · Cited by 91 · see citing works
Self-citationCharacterization and prediction of protein–protein interactions within and between complexes
Sprinzak, E., Altuvia, Y., & Margalit, H. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0603352103 · Cited by 86 · see citing works
Validation and refinement of gene-regulatory pathways on a network of physical interactions
Yeang, C., Mak, H. C., McCuine, S., Workman, C. T., Jaakkola, T., & Ideker, T. E. (2005) · Genome biology · doi:10.1186/gb-2005-6-7-r62 · Cited by 83 · see citing works
Self-citationRoberts, L. (2001) · Science · doi:10.1126/science.291.5507.1182a · Cited by 82 · see citing works
Deciphering principles of transcription regulation in eukaryotic genomes
Nguyen, D., & D’haeseleer, P. (2006) · Molecular Systems Biology · doi:10.1038/msb4100054 · Cited by 79 · see citing works
A History of the Human Genome Project
Roberts, L., Davenport, R., Pennisi, E. M., & Marshall, E. (2001) · Science · doi:10.1126/science.291.5507.1195 · Cited by 78 · see citing works
Classifying by colors: FISH-based genome analysis
Fauth, C., & Speicher, M. R. (2001) · Cytogenetic and Genome Research · doi:10.1159/000056937 · Cited by 64 · see citing works
Whelan, D. B. (1998) · PubMed · Cited by 59 · see citing works
Transcriptional regulation of protein complexes within and across species
Tan, K., Shlomi, T., Feizi, H., Ideker, T., & Sharan, R. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606914104 · Cited by 58 · see citing works
Self-citationCombinatorial RNAi for quantitative protein network analysis
Şahin, Ö., Löbke, C., Korf, U., Appelhans, H., Sültmann, H., Poustka, A., Wiemann, S., & Arlt, D. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606827104 · Cited by 55 · see citing works
Techniques patents for SNP genotyping
Twyman, R. M., & Primrose, S. B. (2003) · Pharmacogenomics · doi:10.1517/phgs.4.1.67.22582 · Cited by 54 · see citing works
Ordering gene function: the interpretation of epistasis in regulatory hierarchies
Avery, L. (1992) · Trends in Genetics · doi:10.1016/0168-9525(92)90140-y · Cited by 51 · see citing works
Problems with genome-wide association studies.
Williams, S. M., Canter, J. A., Crawford, D. C., Moore, J. H., Ritchie, M. D., & Haines, J. L. (2007) · PubMed · Cited by 50 · see citing works
Prediction of phenotype and gene expression for combinations of mutations
Carter, G. W., Prinz, S., Neou, C., Shelby, J. P., Marzolf, B., Thórsson, V., & Galitski, T. (2007) · Molecular Systems Biology · doi:10.1038/msb4100137 · Cited by 47 · see citing works
Green, P. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082095999 · Cited by 46 · see citing works
Bourgain, C., Génin, E., Cox, N. J., & Clerget‐Darpoux, F. (2006) · European Journal of Human Genetics · doi:10.1038/sj.ejhg.5201753 · Cited by 38 · see citing works
CELLS, GELS AND THE ENGINES OF LIFE
Coombe, D. R. (2002) · Immunology and Cell Biology · doi:10.1046/j.1440-1711.2002.01110.x · Cited by 36 · see citing works
Downard, K. M. (2006) · PROTEOMICS · doi:10.1002/pmic.200600247 · Cited by 36 · see citing works
Identification and characterization of protein subcomplexes in yeast
Hollunder, J., Beyer, A., & Wilhelm, T. E. (2005) · PROTEOMICS · doi:10.1002/pmic.200401121 · Cited by 22 · see citing works
Self-citationDisentangling information flow in the Ras-cAMP signaling networkPreprint
Carter, G. W., Rupp, S., Fink, G. R., & Galitski, T. (2006) · Genome Research · doi:10.1101/gr.4473506 · Cited by 19 · see citing works
Bao, L., Wei, L., Peirce, J. L., Homayouni, R., Li, H., Zhou, M., Chen, H., Lu, L., Williams, R. W., Pfeffer, L. M., et al. (2006) · Mammalian Genome · doi:10.1007/s00335-005-0172-2 · Cited by 18 · see citing works
Simulation of quantitative characters from qualitatively acting genes
Jana, S. (1972) · Theoretical and Applied Genetics · doi:10.1007/bf00583413 · Cited by 17 · see citing works
Punnett, R. C. (1913) · The Macmillan company eBooks · doi:10.5962/bhl.title.57214 · Cited by 3 · see citing works
Flagged first, then by citations
3 of the 125 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Phillips, P. C. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2452 · Cited by 1,570 · see citing works
“The obvious next step for these analyses is to link the network structure revealed by epistasis analysis to information obtained from other methods, such as yeast two-hybrid, chromatin immunoprecipitation and gene expression assays, to build a comprehensive map of the full 'interactome”
MethodArchitecture of the human regulatory network derived from ENCODE data
Gerstein, M., Kundaje, A. B., Hariharan, M., Landt, S. G., Yan, K., Cheng, C., Mu, X. J., Khurana, E., Rozowsky, J. S., Alexander, R. P., et al. (2012) · Nature · doi:10.1038/nature11245 · Cited by 1,418 · see citing works
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq
Jaitin, D. A., Weiner, A., Yofe, I., Lara-Astiaso, D., Keren-Shaul, H., David, E., Salame, T. M., Tanay, A., van Oudenaarden, A., & Amit, I. (2016) · Cell · doi:10.1016/j.cell.2016.11.039 · Cited by 777 · see citing works
“Cellular diversity and response are controlled by regulatory networks, which function as decision-making circuits of the cell (Amit et al., 2011; Beyer et al., 2007; Carter et al., 2013).”
Next-generation genomics: an integrative approach
Hawkins, R. D., Hon, G. C., & Ren, B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2795 · Cited by 591 · see citing works
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 · see citing works
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 505 · see citing works
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works
Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins
Hu, P., Janga, S. C., Babu, M., Díaz-Mejía, J. J., Butland, G., Yang, W., Pogoutse, O., Guo, X., Phanse, S., Wong, P., et al. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000096 · Cited by 387 · see citing works
“Indeed, it has been shown previously that the combination of PI with functional genomic inferences, each statistically weighted according to dataset quality, can markedly improve both functional coverage and accuracy [59,61,70–72].”
BackgroundMuxViz: a tool for multilayer analysis and visualization of networks
Domenico, M. D., Porter, M. A., & Arenas, A. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu038 · Cited by 419 · see citing works
“Such efforts have yielded numerous insights in many areas of science and technology [2–16].”
BackgroundYeger‐Lotem, E., Riva, L., Su, L. J., Gitler, A. D., Cashikar, A. G., King, O. D., Auluck, P. K., Geddie, M. L., Valastyan, J. S., Karger, D. R., et al. (2009) · Nature Genetics · doi:10.1038/ng.337 · Cited by 284 · see citing works
“We assembled an integrated network model of the yeast interactome that contains protein–protein interactions, metabolic relations and protein–DNA interactions detected by various methods with different levels of reliabilit”
eSGA: E. coli synthetic genetic array analysis
Butland, G., Babu, M., Díaz-Mejía, J. J., Bohdana, F., Phanse, S., Gold, B., Yang, W., Li, J., Gagarinova, A. G., Pogoutse, O., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1239 · Cited by 256 · see citing works
“, and illuminate the modular arrangement of functional neighborhood”
BackgroundToward the dynamic interactome: it's about time
Przytycka, T. M., Singh, M., & Slonim, D. K. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbp057 · Cited by 252 · see citing works
MuxViz: a tool for multilayer analysis and visualization of networks
Domenico, M. D., Porter, M. A., & Arenas, A. (2014) · Cited by 249 · see citing works
Shadows of complexity: what biological networks reveal about epistasis and pleiotropy
Tyler, A. L., Asselbergs, F. W., Williams, S. M., & Moore, J. H. (2009) · BioEssays · doi:10.1002/bies.200800022 · Cited by 185 · see citing works
“This work further supports the importance of understanding the network, and not just a single gene.((56)) Together, these observations of gene network function offer strong support for the hypothesis that epistasis and pleiotropy are the result, not of single genes or single pairs of genes, but of the effects of genes…”
ResultLi, J., Zhu, X., & Chen, J. Y. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000450 · Cited by 185 · see citing works
“Here, the concept of studying ‘‘inter-class’’ molecular associations is quite different from that of ‘‘intra-class’’ molecular associations such as gene-gene interactions, drug-drug interactions, or protein-protein interactions [3].”
BackgroundGlobal networks of functional coupling in eukaryotes from comprehensive data integrationPreprint
Alexeyenko, A., & Sonnhammer, E. L. L. (2009) · Genome Research · doi:10.1101/gr.087528.108 · Cited by 158 · see citing works
“…from e.g. protein-protein interactions, mRNA co-expression have been integrated into interaction networks in such organisms as S. cerevisiae, C. elegans, and H. sapiens (Jansen et al., 2003; Troyanskaya et al., 2003; Bader et al., 2004; Lee et al., 2004; Li et l., 2004; Beyer et al., 2007).”
BackgroundThe Cancer Cell Map Initiative: Defining the Hallmark Networks of Cancer
Krogan, N. J., Lippman, S. M., Agard, D. A., Ashworth, A., & Ideker, T. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.05.008 · Cited by 151 · see citing works
“…of physical and genetic interaction data is a powerful way to reconstruct biological pathways (Bandyopadhyay et al., 2008; Beltrao et al., 2010; Beyer et al., 2007; Collins et al., 2007; Fischer et al., 2015; Gao et al., 2004; Hannum et al., 2009; Kelley and Ideker, 2005; Roguev et al., 2008;…”
BackgroundSelf-citationBourras, S., McNally, K. E., Ben‐David, R., Parlange, F., Roffler, S., Praz, C. R., Oberhaensli, S., Menardo, F., Stirnweis, D., Frenkel, Z., et al. (2015) · The Plant Cell · doi:10.1105/tpc.15.00171 · Cited by 145 · see citing works
“Thus, the wheat powdery mildew physical map can be integrated with the genetic maps we produced, which is a powerful approach for improving genome assembly (Beyer et al., 2007).”
BackgroundFunctional Maps of Protein Complexes from Quantitative Genetic Interaction Data
Bandyopadhyay, S., Kelley, R., Krogan, N. J., & Ideker, T. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000065 · Cited by 141 · see citing works
“Another set of methods has sought to interpret genetic relationships using physical protein-protein interactions [13].”
BackgroundSelf-citationThe role of protein interaction networks in systems biomedicine
Sevimoğlu, T., & Arğa, K. Y. (2014) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.08.008 · Cited by 141 · see citing works
“Complex diseases stem from interplay of genetic and environmental factors, which complicate the study of human diseases, since it is dif fi cult to create a controlled environment that enables scientists to study environmental effects on disease development [64].”
BackgroundDetection and interpretation of expression quantitative trait loci (eQTL)
Michaelson, J. J., Loguercio, S., & Beyer, A. (2009) · Methods · doi:10.1016/j.ymeth.2009.03.004 · Cited by 131 · see citing works
Self-citationPlant Protein-Protein Interaction Network and Interactome
Zhang, Y., Gao, P., & Yuan, J. S. (2009) · Current Genomics · doi:10.2174/138920210790218016 · Cited by 131 · see citing works
“The genetic interaction network is relevant to but different from the so-called protein ‘physical interaction’ network, which is often established based on protein interactions instead of genetic interactions [5,22].”
BackgroundeQED: an efficient method for interpreting eQTL associations using protein networks
Suthram, S., Beyer, A., Karp, R. M., Eldar, Y. C., & Ideker, T. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.4 · Cited by 128 · see citing works
“Integrating eQTL data with additional independent information may significantly reduce the noise and improve the statistical power of the analysis (Beyer et al , 2007).”
MethodSelf-citationNext generation sequencing in functional genomics
Werner, T. A. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbq018 · Cited by 125 · see citing works
Zhang, B., Li, H., Riggins, R. B., Zhan, M., Xuan, J., Zhang, Z., Hoffman, E. P., Clarke, R. C., & Wang, Y. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn660 · Cited by 121 · see citing works
Interaction networks: From protein functions to drug discovery. A review
Chautard, É., Thierry‐Mieg, N., & Ricard‐Blum, S. (2008) · Pathologie Biologie · doi:10.1016/j.patbio.2008.10.004 · Cited by 116 · see citing works
“genetic and physical interactions) that are currently available [87].”
BackgroundBRCA1 and S phase DNA repair pathways restrict LINE-1 retrotransposition in human cells
Mita, P., Sun, X., Fenyö, D., Kahler, D. J., Li, D., Agmon, N., Wudzinska, A., Keegan, S., Bader, J. S., Yun, C., et al. (2020) · Nature Structural & Molecular Biology · doi:10.1038/s41594-020-0374-z · Cited by 113 · see citing works
High-resolution network biology: connecting sequence with function
Ryan, C. J., Cimermančič, P., Szpiech, Z. A., Šali, A., Hernandez, R. D., & Krogan, N. J. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3574 · Cited by 98 · see citing works
“…analyses of protein–protein interaction networks with genetic interaction networks have revealed features that are not evident from studying either type of networks in isolation, and such analyses have offered an improved understanding of the relationship between these two networks 58,61 .”
Large-Scale Profiling of Kinase Dependencies in Cancer Cell Lines
Campbell, J., Ryan, C. J., Brough, R., Bajrami, I., Pemberton, H. N., Chong, I. Y.-S., Costa-Cabral, S., Frankum, J. R., Gulati, A., Holme, H., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.02.023 · Cited by 103 · see citing works
“In model organisms, the problem of interpreting such dependencies has been addressed by integrating information from protein-protein (Beyer et al., 2007) and kinase-substrate interaction databases (Fiedler et al.”
BackgroundMethodCanuel, V., Rance, B., Avillach, P., Degoulet, P., & Burgun, A. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbu006 · Cited by 97 · see citing works
“depth discussion of some of these technologies [2–8].”
BackgroundNesvizhskii, A. I. (2012) · PROTEOMICS · doi:10.1002/pmic.201100537 · Cited by 93 · see citing works
DRYGIN: a database of quantitative genetic interaction networks in yeast
Koh, J. L. Y., Ding, H., Costanzo, M., Baryshnikova, A., Toufighi, K., Bader, G. D., Myers, C. L., Andrews, B., & Boone, C. M. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp820 · Cited by 92 · see citing works
Immunoliposomes for cancer therapy.
Kontermann, R. E. (2006) · PubMed · Cited by 91 · see citing works
Detection of Composite Communities in Multiplex Biological Networks
Bennett, L., Kittas, A., Muirhead, G., Papageorgiou, L. G., & Tsoka, S. (2015) · Scientific Reports · doi:10.1038/srep10345 · Cited by 80 · see citing works
“The physical and genetic interactions of the two yeast networks can be regarded as complementary, i.e. physical interactions represent direct spatial associations whereas genetic interactions describe similar functional role 42 .”
BackgroundAn annotated genetic map of loblolly pine based on microsatellite and cDNA markers
Echt, C. S., Saha, S., Krutovsky, K. V., Wimalanathan, K., Erpelding, J. E., Liang, C., & Nelson, C. D. (2011) · BMC Genetics · doi:10.1186/1471-2156-12-17 · Cited by 75 · see citing works
“Genetic maps, when integrated with physical maps, have played an important role in genome assembly for other plant species [6,7] and, when annotated for gene function, are important for mapping cellular regulatory and signalling networks [8].”
BackgroundKorcsmáros, T., Farkas, I. J., Szalay, M., Rovó, P., Fazekas, D., Spiró, Z., Böde, C., Lenti, K., Vellai, T., & Csermely, P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq310 · Cited by 72 · see citing works
Genomic resources in horticultural crops: Status, utility and challenges
Sonah, H., Deshmukh, R. K., Singh, V. P., Gupta, D. K., Singh, N. K., & Sharma, T. R. (2010) · Biotechnology Advances · doi:10.1016/j.biotechadv.2010.11.002 · Cited by 71 · see citing works
“Such integration accelerated the process of fine mapping, positional cloning, comparative genomeanalysis, and clone-by-clone sequencing of different species (Beyer et al., 2007; Kim et al., 2010; Cova et al., 2010).”
MethodFrom E-MAPs to module maps: dissecting quantitative genetic interactions using physical interactions
Ulitsky, I., Shlomi, T., Kupiec, M., & Shamir, R. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.42 · Cited by 80 · see citing works
“This integration is particularly appealing due to the complementarity of the two interaction types: PIs describe direct spatial association between molecules, whereas GIs refer to functional associations between genes, connecting the physical architecture to phenotypes (Beyer et al , 2007).”
BackgroundMetabolome 2.0: quantitative genetics and network biology of metabolic phenotypes
Dumas, M. (2012) · Molecular BioSystems · doi:10.1039/c2mb25167a · Cited by 62 · see citing works
Zhu, X. (2011) · International Journal of Oncology · doi:10.3892/ijo.2011.1172 · Cited by 56 · see citing works
“However, highthroughput also indicates highly-complex and noise-generating, how to systematically investigate these data and avoid those that might be selected by chance becomes a challenge (9,10).”
BackgroundMethodSharma, S., & Petsalaki, E. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19040933 · Cited by 54 · see citing works
PARK7/DJ-1 promotes pyruvate dehydrogenase activity and maintains Treg homeostasis during ageing
Danileviciute, E., Zeng, N., Capelle, C., Paczia, N., Gillespie, M. A. K., Kurniawan, H., Benzarti, M., Merz, M. P., Coowar, D., Fritah, S., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00576-y · Cited by 57 · see citing works
Large-scale De Novo Prediction of Physical Protein-Protein Association
Elefsinioti, A. L., Saraç, Ö. S., Hegele, A., Plake, C., Hubner, N. C., Poser, I., Sarov, M., Hyman, A. A., Mann, M., Schroeder, M., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.010629 · Cited by 53 · see citing works
“Knowing physical associations is important for elucidating the structure of pathways and for understanding molecular mechanisms underlying high-level phenotypes (1, 4, 11).”
BackgroundSelf-citationSpidermiR: An R/Bioconductor Package for Integrative Analysis with miRNA Data
Cava, C., Colaprico, A., Bertoli, G. R., Graudenzi, A., Silva, T. C., Olsen, C., Noushmehr, H., Bontempi, G., Mauri, G., & Castiglioni, I. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18020274 · Cited by 53 · see citing works
“PIs are referred to as protein–protein interactions, and are crucial for assessing the structural and functional architecture of the cell in terms of how direct associations between molecules constitute protein complexes, signal transduction pathways and other cellular machinery [42].”
BackgroundDynamic Zebrafish Interactome Reveals Transcriptional Mechanisms of Dioxin Toxicity
Alexeyenko, A., Wassenberg, D. M., Lobenhofer, E. K., Yen, J. H., Linney, E. A., Sonnhammer, E. L. L., & Meyer, J. N. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010465 · Cited by 52 · see citing works
“Several interactomes, either experimentally elucidated, computationally predicted, or their combinations, have been released for a number of eukaryotes [41].”
BackgroundLiu, Y., Koyutürk, M., Barnholtz‐Sloan, J. S., & Chance, M. R. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-65 · Cited by 52 · see citing works
“Interactions are associated with several dynamic characteristics, such as their direction, strength, permanence or transience, and presence or absence [6].”
BackgroundCore module biomarker identification with network exploration for breast cancer metastasis
Yang, R., Daigle, B. J., Petzold, L. R., & Doyle, F. J. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-12 · Cited by 49 · see citing works
“The expression changes enacted on the driver genes lead to a cascade of changes of other genes: initially to their first-degree interaction neighbors [4], followed by downstream effects to so-called “passenger” genes.”
BackgroundPredicting biological networks from genomic data
Harrington, E. D., Jensen, L. J., & Peer Bork, B. (2008) · FEBS Letters · doi:10.1016/j.febslet.2008.02.033 · Cited by 47 · see citing works
“For example, in a genetic interaction network two genes are linked by an edge if one gene influences the phenotypic effects of the other [19].”
BackgroundInfluentialDeciphering early development of complex diseases by progressive module network
Zeng, T., Zhang, C.-C., Zhang, W., Liu, R., Liu, J., & Chen, L. (2014) · Methods · doi:10.1016/j.ymeth.2014.01.021 · Cited by 46 · see citing works
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 · see citing works
“Motivated by this, a number of techniques have been developed and applied to infer different types of interactions, such as physical protein–protein interactions (PPIs) (5,6) or genetic interactions (7,8).”
MethodGene regulatory networks in disease and ageing
Avila, P. U., Padvitski, T., Leote, A. C., Chen, H., Sáez-Rodríguez, J., Kann, M., & Beyer, A. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00849-7 · Cited by 44 · see citing works
“However, network edges do not necessarily reflect direct physical interactions between molecules, but can reflect functional relationships between genes or gene products 154 .”
Self-citationOu, J., Tang, S. Q., Sun, D., & Zhang, Y. (2009) · Poultry Science · doi:10.3382/ps.2008-00497 · Cited by 44 · see citing works
Zeng, T., Wang, D. C., Wang, X., Xu, F., & Chen, L. (2014) · Drug Resistance Updates · doi:10.1016/j.drup.2014.08.002 · Cited by 42 · see citing works
“The other dataset is the bio-molecular interactome named physical interaction network (PIN) (Beyer et al., 2007).”
InfluentialDDN: a caBIG® analytical tool for differential network analysis
Zhang, B., Tian, Y., Lu, J., Li, H., Shih, I., Madhavan, S., Clarke, R. C., Hoffman, E. P., Xuan, J., Hilakivi-Clarke, L. A., et al. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr052 · Cited by 41 · see citing works
Advances of genomic science and systems biology in renal transplantation: a review
Perkins, D. L., Verma, M., & Park, K. J. (2011) · Seminars in Immunopathology · doi:10.1007/s00281-011-0243-2 · Cited by 38 · see citing works
“To increase the harvesting of biological information, one approach has been to link modulated genes into signaling pathways using interaction networks or KEGG pathways [47, 48].”
MethodLarge-scale protein interactome mapping: strategies and opportunities
Lievens, S., Eyckerman, S., Lemmens, I. H., & Tavernier, J. H. (2010) · Expert Review of Proteomics · doi:10.1586/epr.10.30 · Cited by 38 · see citing works
Neighbor communities in drug combination networks characterize synergistic effect
Zou, J., Ji, P., Zhao, Y., Li, L., Wei, Y.-Q., Chen, Y., & Yang, S. (2012) · Molecular BioSystems · doi:10.1039/c2mb25267h · Cited by 38 · see citing works
Coiras, M., Camafeita, E., López‐Huertas, M. R., Calvo, E., López, J. A., & Alcamı́, J. (2008) · PROTEOMICS · doi:10.1002/pmic.200700664 · Cited by 37 · see citing works
“This approach allows the identification of differentially expressed proteins with good throughput and sensitivity at the femtomole level.”
BackgroundSystems and Synthetic biology: tackling genetic networks and complex diseases
Cuccato, G., Gatta, G. D., & di Bernardo, D. (2009) · Heredity · doi:10.1038/hdy.2009.18 · Cited by 36 · see citing works
“Using powerful computational methods, it is now possible to integrate information maps deriving from different biological fields, thus helping in identifying candidate genes and understanding their functions (Beyer et al., 2007). Many examples of these new approaches to answer classical genetics questions can be found…”
MethodModularity and directionality in genetic interaction maps
Jaimovich, A., Rinott, R., Schuldiner, M., Margalit, H., & Friedman, N. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq197 · Cited by 32 · see citing works
Iyer, K. V., Maharana, S., Gupta, S., Libchaber, A. J., Tlusty, T., & Shivashankar, G. V. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046628 · Cited by 32 · see citing works
“Interestingly, these correlations are found both at the scale of transcriptome and at the scale of separate transcriptional networks [54].”
BackgroundBabu, M., Musso, G. A., Díaz-Mejía, J. J., Butland, G. P., Greenblatt, J. F., & Emili, A. (2009) · Molecular BioSystems · doi:10.1039/b907407d · Cited by 32 · see citing works
Linking Genes to Cardiovascular Diseases: Gene Action and Gene–Environment Interactions
Pasipoularides, A. D. (2015) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-015-9658-9 · Cited by 31 · see citing works
“Having identified the constituents of the system, a large assortment of techniques can be employed on a gene-by-gene basis, frequently in a hypothesis-driven manner, to determine which components interact, how and, where possible, in what order [51].”
BackgroundMu, Y., Cai, P., Hu, S., Ma, S., & Gao, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088286 · Cited by 31 · see citing works
“Although a lot of information on PPIs has been released publicly on the benefit of advances in proteomics technologies [3], the identification of promiscuous weak or transient protein interactions, which may play critical roles in cellular physiology, remains a challenge [4].”
BackgroundIntegrating Computational and Biochemical Studies to Explore Mechanisms in NF-κB Signaling
Kearns, J. D., & Hoffmann, A. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.r800008200 · Cited by 29 · see citing works
Wang, H., Cai, H., Ao, L., Yan, H., Zhao, W., Qi, L., Gu, Y., & Guo, Z. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv030 · Cited by 29 · see citing works
Predicting chemotherapeutic drug combinations through gene network profiling
Nguyen, T. T. T., Chua, J. K. K., Seah, K. S., Koo, S. H., Yee, J. Y., Yang, E. G., Lim, K. K., Pang, S., Yuen, A., Zhang, L., et al. (2016) · Scientific Reports · doi:10.1038/srep18658 · Cited by 27 · see citing works
An Information Theoretic Approach to Constructing Robust Boolean Gene Regulatory Networks
Vasic, B. V., Ravanmehr, V., & Krishnan, A. R. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.61 · Cited by 27 · see citing works
Ashraf, N., Basu, S., Narula, K., Ghosh, S., Tayal, R., Gangisetty, N., Biswas, S., Aggarwal, P., Chakraborty, N., & Chakraborty, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-19919-5 · Cited by 26 · see citing works
Towards accurate imputation of quantitative genetic interactions
Ulitsky, I., Krogan, N. J., & Ron Shamir, A. (2009) · Genome biology · doi:10.1186/gb-2009-10-12-r140 · Cited by 25 · see citing works
“Genetic interactions (GIs) describe the phenotype of a double knock-out in comparison to the phenotypes of single mutants, and they can be crudely classified into positive (alleviating), neutral, and negative (aggravating) interactions [1,2].”
Backgroundk-Clique Percolation and Clustering
Palla, G., Ábel, D., Farkas, I. J., Pollner, P., Derényi, I., & Vicsek, T. (2008) · Bolyai Society mathematical studies · doi:10.1007/978-3-540-69395-6_9 · Cited by 24 · see citing works
“Over the past decade in biology, especially in Bioinformatics and Systems Biology, the network approach has become very popular and successful [40, 7, 10 , 50, 90, 2]. The CPM (together with its free software implementation, CFinder [65, 1], capable of detecting and visualising k-clique percolation clusters) provides…”
BackgroundÇolak, R., Moser, F., Chu, J. S.-C., Schönhuth, A., Chen, N., & Ester, M. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013348 · Cited by 24 · see citing works
“Note also that genetic interaction data and physical protein-protein interaction data are often complementary [7].”
BackgroundInfluentialSrivas, R., Hannum, G., Ruscheinski, J., Ono, K., Wang, P., Smoot, M. E., & Ideker, T. (2011) · Nature Protocols · doi:10.1038/nprot.2011.368 · Cited by 23 · see citing works
“368" language="eng" publish="issue" relation="no"> Introduction Genetic interactions are defined as functional relationships between genes that result when the phenotypic effect of one gene is altered by one or several other gene”
BackgroundSelf-citationSignalogs: Orthology-Based Identification of Novel Signaling Pathway Components in Three Metazoans
Korcsmaros, T. D., Szalay, M., Rovó, P., Palotai, R., Fazekas, D., Lenti, K., Farkas, I. J., Csermely, P., & Vellai, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019240 · Cited by 23 · see citing works
“Currently, studies in model organisms ranging from invertebrates to mammals are increasingly used to create such a network [3].”
BackgroundExploring drug-target interaction networks of illicit drugs
Atreya, R. V., Sun, J., & Zhao, Z. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s4-s1 · Cited by 23 · see citing works
One for all and all for One: Improving replication of genetic studies through network diffusion
Lancour, D., Naj, A. C., Mayeux, R. P., Haines, J. L., Pericak‐Vance, M. A., Schellenberg, G. D., Crovella, M. E., Farrer, L. A., & Kasif, S. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007306 · Cited by 22 · see citing works
Update of KDBI: Kinetic Data of Bio-molecular Interaction database
Kumar, P., Han, B. C., Shi, Z., Jia, J., Wang, Y., Zhang, Y., Liang, L., Liu, Q. F., Ji, Z., & Chen, Y. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn839 · Cited by 22 · see citing works
A network perspective on unraveling the role of TRP channels in biology and disease
Chun, J. N., Lim, J. M., Kang, Y. A., Kim, E. H., Shin, Y.-C., Kim, H., Jang, D., Kwon, D., Shin, S.-Y., So, I., et al. (2013) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-013-1292-2 · Cited by 21 · see citing works
“[4, 9, 95].”
BackgroundInsights into the genome structure and copy-number variation of Eimeria tenella
Lim, L., Tay, Y.-L., Alias, H., Wan, K., & Dear, P. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-389 · Cited by 18 · see citing works
Arif, A. S., Manshaei, R., DeLong, S., East, B. G., Kyan, M. J., & Mazalek, A. (2016) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/2839462.2839500 · Cited by 20 · see citing works
“A physical interaction refers to the experience where a direct physical association between two gene products (proteins) has been identified, while a genetic interaction is identified when the effect of mutating one gene is reflected by the perturbation in another gene [2].”
BackgroundIdentification of VIMP as a Gene Inhibiting Cytokine Production in Human CD4+ Effector T Cells
Capelle, C., Zeng, N., Danileviciute, E., Rodrigues, S. F., Ollert, M. W., Balling, R., & HeFeng, F. Q. (2020) · iScience · doi:10.1016/j.isci.2021.102289 · Cited by 19 · see citing works
“association theory (26-28), to predict novel key genes of a given biological process or function and have successfully applied it to human CD4CD25CD127 regulatory T cells (Tregs) (29, 30).”
Systematic Interpretation of Comutated Genes in Large-Scale Cancer Mutation Profiles
Gu, Y., Yang, D., Zou, J., Ma, W., Wu, R., Zhao, W., Zhang, Y., Xiao, H., Gong, X., Zhang, M., et al. (2010) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-10-0022 · Cited by 20 · see citing works
Quantitative maps of genetic interactions in yeast - Comparative evaluation and integrative analysis
Lindén, R., Eronen, V.-P., & Aittokallio, T. A. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-45 · Cited by 18 · see citing works
“…under different environmental conditions, using fitness phenotypes other than growth, or on multiple perturbations or study organisms to investigate questions related, for instance, to plasticity and evolution of genetic networks or higher-order and interspecies interactions [2,3,17,41-47].”
BackgroundA New Class of Autopoietic and Cognitive Machines
Mikkilineni, R. (2022) · Information · doi:10.3390/info13010024 · Cited by 18 · see citing works
McDermott, J., Costa, M. N., Janszen, D. B., Singhal, M., & Tilton, S. C. (2010) · Disease Markers · doi:10.1155/2010/708932 · Cited by 18 · see citing works
Visualizing evolution and impact of biomedical fields
Cokol, M., & Rodriguez‐Esteban, R. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.05.002 · Cited by 18 · see citing works
Computational Prediction of Protein Complexes from Protein Interaction Networks
Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650 · Cited by 17 · see citing works
Evolutionarily Conserved Network Properties of Intrinsically Disordered Proteins
Rangarajan, N., Kulkarni, P., & Hannenhalli, S. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126729 · Cited by 17 · see citing works
“Recent availability of large-scale interaction data as well as other high-throughput datasets [2] have fuelled systems-level approaches that apply network theory to gain insights into how biological systems are organized to facilitate robust and rapid information processing [3].”
BackgroundGene Mining for Proline Based Signaling Proteins in Cell Wall of Arabidopsis thaliana
Ihsan, M. Z., Ahmad, S. J. N., Shah, Z. H., Rehman, H. M., Aslam, Z., Ahuja, I., Bones, A. M., & Ahmad, J. N. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.00233 · Cited by 17 · see citing works
Yang, T.-H., & Wu, W. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-s6-s13 · Cited by 17 · see citing works
“Cellular responses to external stimuli or environmental changes are usually conveyed through cellular regulatory networks consisting of different regulatory pathways [1-4].”
BackgroundBabu, M., Gagarinova, A. G., Greenblatt, J. F., & Emili, A. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-197-0_9 · Cited by 16 · see citing works
Gopalacharyulu, P. V., Velagapudi, V., Lindfors, E., Halperin, E., & Orešič, M. (2009) · Molecular BioSystems · doi:10.1039/b815347g · Cited by 16 · see citing works
Goldfarb, D., Hast, B. E., Wang, W., & Major, M. B. (2014) · Journal of Proteome Research · doi:10.1021/pr5008416 · Cited by 15 · see citing works
Signaling pathway impact analysis by incorporating the importance and specificity of genes (SPIA-IS)
Fang, H., Li, X., Zan, X., Shen, L., Ma, R., & Liu, W. (2017) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2017.09.009 · Cited by 15 · see citing works
Babu, M., Gagarinova, A. G., & Emili, A. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-276-2_7 · Cited by 15 · see citing works
Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity
Kim, J.-K., & Tan, K. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-521 · Cited by 14 · see citing works
“Recent advances in proteomics technologies such as two-hybrid system and mass spectrometry has allowed enormous amount of data on protein-protein interactions (PPI) to be released into the public domain [1].”
BackgroundSystematic Detection of Epistatic Interactions Based on Allele Pair Frequencies
Ackermann, M., & Beyer, A. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002463 · Cited by 14 · see citing works
“In addition, epistatic interactions provide important insights into the functional organization of molecular pathways [2,3].”
BackgroundSelf-citationFenger, M., Linneberg, A. R., Werge, T. M., & Jørgensen, T. (2008) · BMC Genetics · doi:10.1186/1471-2156-9-43 · Cited by 14 · see citing works
“An alternative is to model networks of interacting genes, gene products, and regulatory structures covering the entire genome [6-8,39], but even in mono-cellular organisms this is a daunting task and is far from being resolved.”
BackgroundGenetic networks for the functional study of genomes
Pisabarro, A. G., Pérez, G., Lavín, J. L., & Ramı́rez, L. (2008) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/eln026 · Cited by 14 · see citing works
“) [16, 21].”
Chatr‐aryamontri, A., Winter, A. G., Perfetto, L., Briganti, L., Licata, L., Iannuccelli, M., Castagnoli, L., Cesareni, G., & Tyers, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s8-s8 · Cited by 13 · see citing works
“High-throughput technologies have recently permitted the rapid accumulation of vast collections of genome-scale data for mRNA expression [10], protein post-translational modifications [11], protein-nucleic acid interactions, protein-protein interactions and genetic interactions [12-14].”
Background
- Authors
- Beyer, A., Bandyopadhyay, S., & Ideker, T.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Ideker, Trey
- Venue
- Nature Reviews Genetics
- Published
- 2007-09
- Volume
- 8
- Issue
- 9
- Pages
- 699-710
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC2811081
- OpenAlex
- W2146997276
- Cited by
- 207
- References
- 125
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 8 · 2017: 20 · 2016: 8 · 2015: 10 · 2014: 15 · 2013: 11 · 2012: 23
- FWCI
- 1.74
- 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 Institute of Environmental Health Sciences (ES014811, R01ES014811)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- physical map, genetic linkage map, interaction networks, network integration, genetic interactions, protein-protein interaction, pathway models, interaction classification
- MeSH terms
- Genetic Techniques, Humans, Models, Biological, Signal Transduction, Genome, Human, Physical Chromosome Mapping, 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
