Resultados obtenidos para los algoritmos basados en HAMMING

Networks analysis is a broadly applicable tool for the drug discovery and development process. Any type of association data linking one gene to another, a protein or a compound, can be modeled, visualized and analyzed as networks (Lee et al., 2004, Chua et al., 2007, Lee et al., 2008a, Linghu et al., 2009, Lee et al., 2010, McGary et al., 2010, Wu et al., 2010, Lee et al., 2011). Hence, data from pre-clinical and clinical trial studies can be included in network analyses (Nikolsky et al., 2005). Thus, networks could represent the standard for data integration and analysis. Network analysis involving neglected-disease pathogens is a very young area of research. Moreover, despite the availability experimentally PINs of model organisms as S. cerevisiae, C. elegans, and D. melanogaster, and some bacterial pathogens like H. pylori, C. jejuni, Treponema pallidum, the number of experimentally neglected-disease pathogens PINs is limited. For example, LaCount et al., (2005) identified protein-protein interactions of P. falciparum through a high throughput screening version of the yeast two- hybrid system (LaCount et al., 2005). They found 2,846 unique interactions in more than 32,000 P. falciparum protein fragments. In order to determine clusters of interacting proteins they used computational methods such as analysis of network connectivity, gene co- expression, and enrichment of Gene Ontology terms. The results of the network analysis was the identification of two protein clusters, one of which related to the chromatin modification, transcription, messenger RNA stability, and ubiquitination and the other

implicated in the invasion of host cells. They suggested that the information provided by this network may be relevant to understand the basic biology of the parasite and to discover new drug and vaccine targets. Wang et al., (2010) built a PIN of the M. tuberculosis H37Rv strain based on a high-throughput bacterial two-hybrid method. They found more than 8,000 novel interactions and performed a cross-species PINs comparison, showing 94 conserved sub-networks between M. tuberculosis and several prokaryotic PINs (Wang et al., 2010).

Additionally, even the lack of data, several computational studies aims to predict PINs of neglected-disease pathogens and prioritize drug targets have been performed. Florez et al., (2010) built an in silico PIN of L. major by combining information of PSIMAP, PEIMAP, iPfam databases, and using the interologs method (Florez et al., 2010). They predicted 33,861 interactions for 1,366 proteins, and also analyzed the PIN by calculating topology parameters such as connectivity and betweenness centrality detecting 142 potential and specific drug targets without human orthologs (Fig. 8). Pedamallu and Posfai (2010) have developed a simple open source package module (OpenPPI_predictor) to predict putative PIN for target genomes (Pedamallu and Posfai 2010). The package is based on interologs method and uses experimental data from a related organism. Thus, they assayed OpenPPI_predictor to infer a PIN for B. malayi using experimental PIN data from C. elegans. They identified 118 and 143 clusters in B. malayi and C. elegans interactomes,

Fig. 8. Predicted PIN of Leishmania major by Florez et al., (2010). The nodes in color red represent predicted essential proteins without human orthologs.

respectively, and found that highly connected region contains 363 and 340 proteins in B. malayi and C. elegans PINs. They suggests that core cellular functions of the two related organisms have similar complexity and that further analysis of these highly connected regions may provide clues about genes missing from a conserved pathway, or proteins missing from a complex.

Similarly, computational studies have been developed in order to model host-neglected- disease pathogens PINs. For example, Dyer et al., (2007) integrated public intra-species PINs datasets with protein–domain profiles to predict a Human–P. falciparum PIN. They found 516 protein interactions between these two organisms, and showed that Plasmodium proteins interacting with human proteins are co-expressed in DNA microarray datasets, associated with developmental stages of the Plasmodium life cycle (Dyer et al., 2007). Dyer et al., (2008) have analyzed the landscape of human proteins interacting with pathogens. They integrated human–pathogen PINs for 190 pathogen strains from seven public databases and found that both viral and bacterial pathogens tend to interact with proteins with many interacting partners (hubs) and those that are central to many paths (bottlenecks) in the human PIN (Dyer et al., 2008). Similar results were obtained by Navratil et al., (2011). They used a high- quality dataset manually curated and validated of virus-host protein interactions to depict the “human infectome” (Navratil et al., 2011). Additionally, they showed, by using functional genomic RNAi data, that the high centrality of targeted proteins was correlated to their essentiality for viruses’ lifecycle. Also, they perform a simulation of cellular network perturbations and showed a stealth-attack of viruses on proteins bridging cellular functions, which is a property that could be essential in the molecular etiology of some human diseases (Fig. 9). Doolittle and Gomez (2011) have predicted interactions between dengue

virus (DENV) and its hosts, both human and the insect vector Aedes aegypti. They implemented a protocol based on structural similarity between DENV and host proteins, and also they supported a subset of the predictions via mining from the literature. They predicted, after filtering and based on shared Gene Ontology cellular component, over 2,000 interactions between DENV and humans, as well as 18 interactions between DENV and the A. aegypti vector (Doolittle and Gomez 2011). They suggested those specific interactions between virus and host proteins are involved in interferon signaling, transcriptional regulation, stress, and the unfolded protein response.

The most relevant outcome of such computational studies is the identification of human and pathogen proteins to target experimentally for developing new drugs. It also provides different roadmaps and emerging approaches to develop projects to model and analyze PINs of neglected-disease pathogens. For example, novel therapies for human diseases employ multi-target drugs (Borisy et al., 2003, Csermely et al., 2005) and compounds targeted to inhibit protein-protein interactions (Emerson et al., 2003, Klein and Vassilev 2004, Vassilev 2004, Vassilev et al., 2004).

5. Conclusions

Because of the development of massive analysis technologies in genomics and computational biology, we can outline a trend to interplay and integrate the computational and experimental techniques. Thus, the methods and resources to identify protein interactions that combine both approaches will be used as a routine protocol in the future. Even though the use of network biology approaches to drug discovery are in their initial stages, they already contributed to meaningful drug development decisions by accelerating hypothesis-driven biology, modeling specific physiologic problems in target validation or clinical physiology and, providing rapid characterization and interpretation of disease- relevant cell systems.

Despite the lack of experimental functional genomics and PINs data for neglected-disease pathogens, computational approaches represent a starting point and complementary approach to current high-throughput screening projects whose aim is to delineate the complete genomes of neglected-disease pathogens. Moreover, integrative computational approaches have shown to be a powerful tool as guide for large scale-studies improving and facilitating the rational identification of therapeutic targets.

It is clear that for those organisms whose genome has not been sequenced yet, it will be difficult to implement the aforementioned protocols. That is the case for some nematodes and trypanosomal parasites as T. cruzi, S. mansoni, B. malayi, and O. volvulus, and the soil- transmitted helminthes (e.g., species of A. lumbricoides, and T. trichura). However, according to NCBI Entrez Genome (URL:, http://www.ncbi.nlm.nih.gov/genomes/leuks.cgi; Sep 29, 2011), the status of most of them is in “assembly”stage. Once the genome of the neglected- disease pathogen is available, we can use the information of experimental PINs of model organism as C. elegans to model and predict PINs of such pathogens enabling the discovery of those hubs and bottlenecks proteins that modulate the infectious process and prioritize them as drug targets.

While the computational approaches analyzed here are by nature probabilistic, i.e. it offers the likelihood of association of a given pair of proteins, nevertheless it clearly indicates the utility of inferring functionally relevant correlations from the available genomic databases for systematic drug target identification. The further improvement of computational approaches will help to increasing the availability of systematically collected biologic data and will provide an easy schema for the integration of different types of data within network analysis, thus enhancing the role of such approaches in drug discovery.

Finally, comprehensive repositories of functional genomic data for neglected-disease pathogens will be created. Hence, as soon as large molecular datasets are processed with the help of network analysis, a growing set of predicted pathways and PINs will emerge and will offer a new paradigm for re-thinking about how to revolutionize the drug discovery process.

6. Acknowledgments

The authors thank BioMed Central for allowing the reproduction of figures 8 and 9 (Florez et al., 2010, Navratil et al., 2011). Mario A. Rodríguez-Pérez and Xianwu Guo holds a scholarship from Comisión de Operación y Fomento de Actividades Académicas (COFAA)/IPN.

7. References

Abagyan, R. A., and S. Batalov. (1997). Do aligned sequences share the same fold? J Mol Biol 273: 355-368: Oct 17.

Aguero, F., B. Al-Lazikani, M. Aslett, M. Berriman, F. S. Buckner, R. K. Campbell, S. Carmona, I. M. Carruthers, A. W. Chan, F. Chen, G. J. Crowther, M. A. Doyle, C. Hertz-Fowler, A. L. Hopkins, G. McAllister, S. Nwaka, J. P. Overington, A. Pain, G. V. Paolini, U. Pieper, S. A. Ralph, A. Riechers, D. S. Roos, A. Sali, D. Shanmugam, T. Suzuki, W. C. Van Voorhis, and C. L. Verlinde. (2008). Genomic-scale prioritization of drug targets: the TDR Targets database. Nat Rev Drug Discov 7: 900-907: Nov.

Albert, R. (2005). Scale-free networks in cell biology. J Cell Sci 118: 4947-4957: Nov 1.

Albert, R., and A. L. Barabási. (2002). Statistical mechanics of complex networks. Rev. Modern Phys 1: 30.

Albert, R., H. Jeong, and A. L. Barabasi. (2000). Error and attack tolerance of complex networks. Nature 406: 378-382: Jul 27.

Alon, U. (2007). Network motifs: theory and experimental approaches. Nat Rev Genet 8: 450- 461: Jun.

Alon, U., M. G. Surette, N. Barkai, and S. Leibler. (1999). Robustness in bacterial chemotaxis. Nature 397: 168-171: Jan 14.

Amaral, L. A., A. Scala, M. Barthelemy, and H. E. Stanley. (2000). Classes of small-world networks. Proc Natl Acad Sci U S A 97: 11149-11152: Oct 10.

Barabasi, A. L., and R. Albert. (1999). Emergence of scaling in random networks. Science 286: 509-512: Oct 15.

Barabasi, A. L., and Z. N. Oltvai. (2004). Network biology: understanding the cell's functional organization. Nat Rev Genet 5: 101-113: Feb.

Blumenthal, T. (1998). Gene clusters and polycistronic transcription in eukaryotes. Bioessays 20: 480-487: Jun.

Borisy, A. A., P. J. Elliott, N. W. Hurst, M. S. Lee, J. Lehar, E. R. Price, G. Serbedzija, G. R. Zimmermann, M. A. Foley, B. R. Stockwell, and C. T. Keith. (2003). Systematic discovery of multicomponent therapeutics. Proc Natl Acad Sci U S A 100: 7977-7982: Jun 24.

Bowers, P. M., M. Pellegrini, M. J. Thompson, J. Fierro, T. O. Yeates, and D. Eisenberg. (2004). Prolinks: a database of protein functional linkages derived from coevolution. Genome Biol 5: R35.

Brenner, S. E., C. Chothia, and T. J. Hubbard. (1998). Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships. Proc Natl Acad Sci U S A 95: 6073-6078: May 26.

Buckholz, R. G., C. A. Simmons, J. M. Stuart, and M. P. Weiner. (1999). Automation of yeast two-hybrid screening. J Mol Microbiol Biotechnol 1: 135-140: Aug.

Csermely, P., V. Agoston, and S. Pongor. (2005). The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci 26: 178-182: Apr. Cusick, M. E., N. Klitgord, M. Vidal, and D. E. Hill. (2005). Interactome: gateway into

systems biology. Hum Mol Genet 14 Spec No. 2: R171-181: Oct 15.

Chua, H. N., W. K. Sung, and L. Wong. (2007). An efficient strategy for extensive integration of diverse biological data for protein function prediction. Bioinformatics 23: 3364- 3373: Dec 15.

Dandekar, T., B. Snel, M. Huynen, and P. Bork. (1998). Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem Sci 23: 324-328: Sep. de Folter, S., R. G. Immink, M. Kieffer, L. Parenicova, S. R. Henz, D. Weigel, M. Busscher, M.

Kooiker, L. Colombo, M. M. Kater, B. Davies, and G. C. Angenent. (2005). Comprehensive interaction map of the Arabidopsis MADS Box transcription factors. Plant Cell 17: 1424-1433: May.

Deane, C. M., L. Salwinski, I. Xenarios, and D. Eisenberg. (2002). Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics 1: 349-356: May.

del Rio, G., D. Koschutzki, and G. Coello. (2009). How to identify essential genes from molecular networks? BMC Syst Biol 3: 102.

Devos, D., and A. Valencia. (2001). Intrinsic errors in genome annotation. Trends Genet 17: 429-431: Aug.

Doolittle, J. M., and S. M. Gomez. (2011). Mapping protein interactions between Dengue virus and its human and insect hosts. PLoS Negl Trop Dis 5: e954.

Dunn, R., F. Dudbridge, and C. M. Sanderson. (2005). The use of edge-betweenness clustering to investigate biological function in protein interaction networks. BMC Bioinformatics 6: 39.

Dyer, M. D., T. M. Murali, and B. W. Sobral. (2007). Computational prediction of host- pathogen protein-protein interactions. Bioinformatics 23: i159-166: Jul 1.

Dyer, M. D., T. M. Murali, and B. W. Sobral. (2008). The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog 4: e32: Feb 8.

Emerson, S. D., R. Palermo, C. M. Liu, J. W. Tilley, L. Chen, W. Danho, V. S. Madison, D. N. Greeley, G. Ju, and D. C. Fry. (2003). NMR characterization of interleukin-2 in complexes with the IL-2Ralpha receptor component, and with low molecular weight compounds that inhibit the IL-2/IL-Ralpha interaction. Protein Sci 12: 811- 822: Apr.

Enright, A. J., and C. A. Ouzounis. (2001). Functional associations of proteins in entire genomes by means of exhaustive detection of gene fusions. Genome Biol 2: RESEARCH0034.

Enright, A. J., S. Van Dongen, and C. A. Ouzounis. (2002). An efficient algorithm for large- scale detection of protein families. Nucleic Acids Res 30: 1575-1584: Apr 1.

Erdös, P., and A. Rényi. (1960). On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci: 4.

Fields, S., and O. Song. (1989). A novel genetic system to detect protein-protein interactions. Nature 340: 245-246: Jul 20.

Fitzpatrick, D. A., P. O'Gaora, K. P. Byrne, and G. Butler. (2010). Analysis of gene evolution and metabolic pathways using the Candida Gene Order Browser. BMC Genomics 11: 290.

Florez, A. F., D. Park, J. Bhak, B. C. Kim, A. Kuchinsky, J. H. Morris, J. Espinosa, and C. Muskus. (2010). Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection. BMC Bioinformatics 11: 484.

Freeman, L. C. (1977). Set of measures of centrality based on betweenness. Sociometry 40: 7. Galperin, M. Y., and E. V. Koonin. (1999). Searching for drug targets in microbial genomes.

Curr Opin Biotechnol 10: 571-578: Dec.

Galperin, M. Y., and E. V. Koonin. (2000). Who's your neighbor? New computational approaches for functional genomics. Nat Biotechnol 18: 609-613: Jun.

Galperin, M. Y., and E. V. Koonin. (2004). 'Conserved hypothetical' proteins: prioritization of targets for experimental study. Nucleic Acids Res 32: 5452-5463.

Gandhi, T. K., J. Zhong, S. Mathivanan, L. Karthick, K. N. Chandrika, S. S. Mohan, S. Sharma, S. Pinkert, S. Nagaraju, B. Periaswamy, G. Mishra, K. Nandakumar, B. Shen, N. Deshpande, R. Nayak, M. Sarker, J. D. Boeke, G. Parmigiani, J. Schultz, J. S. Bader, and A. Pandey. (2006). Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat Genet 38: 285-293: Mar.

Gavin, A. C., P. Aloy, P. Grandi, R. Krause, M. Boesche, M. Marzioch, C. Rau, L. J. Jensen, S. Bastuck, B. Dumpelfeld, A. Edelmann, M. A. Heurtier, V. Hoffman, C. Hoefert, K. Klein, M. Hudak, A. M. Michon, M. Schelder, M. Schirle, M. Remor, T. Rudi, S. Hooper, A. Bauer, T. Bouwmeester, G. Casari, G. Drewes, G. Neubauer, J. M. Rick, B. Kuster, P. Bork, R. B. Russell, and G. Superti-Furga. (2006). Proteome survey reveals modularity of the yeast cell machinery. Nature 440: 631-636: Mar 30.

Gavin, A. C., M. Bosche, R. Krause, P. Grandi, M. Marzioch, A. Bauer, J. Schultz, J. M. Rick, A. M. Michon, C. M. Cruciat, M. Remor, C. Hofert, M. Schelder, M. Brajenovic, H. Ruffner, A. Merino, K. Klein, M. Hudak, D. Dickson, T. Rudi, V. Gnau, A. Bauch, S. Bastuck, B. Huhse, C. Leutwein, M. A. Heurtier, R. R. Copley, A. Edelmann, E. Querfurth, V. Rybin, G. Drewes, M. Raida, T. Bouwmeester, P. Bork, B. Seraphin, B. Kuster, G. Neubauer, and G. Superti-Furga. (2002). Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415: 141-147: Jan 10.

Giot, L., J. S. Bader, C. Brouwer, A. Chaudhuri, B. Kuang, Y. Li, Y. L. Hao, C. E. Ooi, B. Godwin, E. Vitols, G. Vijayadamodar, P. Pochart, H. Machineni, M. Welsh, Y. Kong, B. Zerhusen, R. Malcolm, Z. Varrone, A. Collis, M. Minto, S. Burgess, L. McDaniel, E. Stimpson, F. Spriggs, J. Williams, K. Neurath, N. Ioime, M. Agee, E. Voss, K. Furtak, R. Renzulli, N. Aanensen, S. Carrolla, E. Bickelhaupt, Y. Lazovatsky, A. DaSilva, J. Zhong, C. A. Stanyon, R. L. Finley, Jr., K. P. White, M.

Braverman, T. Jarvie, S. Gold, M. Leach, J. Knight, R. A. Shimkets, M. P. McKenna, J. Chant, and J. M. Rothberg. (2003). A protein interaction map of Drosophila melanogaster. Science 302: 1727-1736: Dec 5.

Girvan, M., and M. E. Newman. (2002). Community structure in social and biological networks. Proc Natl Acad Sci U S A 99: 7821-7826: Jun 11.

Hambly, K., J. Danzer, S. Muskal, and D. A. Debe. (2006). Interrogating the druggable genome with structural informatics. Mol Divers 10: 273-281: Aug.

Hernandez-Toro, J., C. Prieto, and J. De las Rivas. (2007). APID2NET: unified interactome graphic analyzer. Bioinformatics 23: 2495-2497: Sep 15.

Ho, Y., A. Gruhler, A. Heilbut, G. D. Bader, L. Moore, S. L. Adams, A. Millar, P. Taylor, K. Bennett, K. Boutilier, L. Yang, C. Wolting, I. Donaldson, S. Schandorff, J. Shewnarane, M. Vo, J. Taggart, M. Goudreault, B. Muskat, C. Alfarano, D. Dewar, Z. Lin, K. Michalickova, A. R. Willems, H. Sassi, P. A. Nielsen, K. J. Rasmussen, J. R. Andersen, L. E. Johansen, L. H. Hansen, H. Jespersen, A. Podtelejnikov, E. Nielsen, J. Crawford, V. Poulsen, B. D. Sorensen, J. Matthiesen, R. C. Hendrickson, F. Gleeson, T. Pawson, M. F. Moran, D. Durocher, M. Mann, C. W. Hogue, D. Figeys, and M. Tyers. (2002). Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415: 180-183: Jan 10.

Hood, L., and R. M. Perlmutter. (2004). The impact of systems approaches on biological problems in drug discovery. Nat Biotechnol 22: 1215-1217: Oct.

Hopkins, A. L., and C. R. Groom. (2002). The druggable genome. Nat Rev Drug Discov 1: 727- 730: Sep.

Huynen, M., B. Snel, W. Lathe, 3rd, and P. Bork. (2000). Predicting protein function by genomic context: quantitative evaluation and qualitative inferences. Genome Res 10: 1204-1210: Aug.

Huynen, M. A., and B. Snel. (2000). Gene and context: integrative approaches to genome analysis. Adv Protein Chem 54: 345-379.

Ideker, T., and R. Sharan. (2008). Protein networks in disease. Genome Res 18: 644-652: Apr. Ito, T., T. Chiba, and M. Yoshida. (2001a). Exploring the protein interactome using

comprehensive two-hybrid projects. Trends Biotechnol 19: S23-27: Oct.

Ito, T., T. Chiba, R. Ozawa, M. Yoshida, M. Hattori, and Y. Sakaki. (2001b). A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci U S A 98: 4569-4574: Apr 10.

Ito, T., K. Ota, H. Kubota, Y. Yamaguchi, T. Chiba, K. Sakuraba, and M. Yoshida. (2002). Roles for the two-hybrid system in exploration of the yeast protein interactome. Mol Cell Proteomics 1: 561-566: Aug.

Jansen, R., H. Yu, D. Greenbaum, Y. Kluger, N. J. Krogan, S. Chung, A. Emili, M. Snyder, J. F. Greenblatt, and M. Gerstein. (2003). A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302: 449-453: Oct 17.

Jeong, H., S. P. Mason, A. L. Barabasi, and Z. N. Oltvai. (2001). Lethality and centrality in protein networks. Nature 411: 41-42: May 3.

Jeong, H., B. Tombor, R. Albert, Z. N. Oltvai, and A. L. Barabasi. (2000). The large-scale organization of metabolic networks. Nature 407: 651-654: Oct 5.

Kanehisa, M., and S. Goto. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28: 27-30: Jan 1.

Kemmeren, P., N. L. van Berkum, J. Vilo, T. Bijma, R. Donders, A. Brazma, and F. C. Holstege. (2002). Protein interaction verification and functional annotation by integrated analysis of genome-scale data. Mol Cell 9: 1133-1143: May.

Klein, C., and L. T. Vassilev. (2004). Targeting the p53-MDM2 interaction to treat cancer. Br J Cancer 91: 1415-1419: Oct 18.

Kohl, P., and D. Noble. (2009). Systems biology and the virtual physiological human. Mol Syst Biol 5: 292.

Koonin, E. V., R. L. Tatusov, and M. Y. Galperin. (1998). Beyond complete genomes: from sequence to structure and function. Curr Opin Struct Biol 8: 355-363: Jun.

LaCount, D. J., M. Vignali, R. Chettier, A. Phansalkar, R. Bell, J. R. Hesselberth, L. W. Schoenfeld, I. Ota, S. Sahasrabudhe, C. Kurschner, S. Fields, and R. E. Hughes. (2005). A protein interaction network of the malaria parasite Plasmodium falciparum. Nature 438: 103-107: Nov 3.

Lee, I., S. V. Date, A. T. Adai, and E. M. Marcotte. (2004). A probabilistic functional network of yeast genes. Science 306: 1555-1558: Nov 26.

Lee, I., U. M. Blom, P. I. Wang, J. E. Shim, and E. M. Marcotte. (2011). Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res 21: 1109-1121: Jul.

Lee, I., B. Lehner, C. Crombie, W. Wong, A. G. Fraser, and E. M. Marcotte. (2008a). A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nat Genet 40: 181-188: Feb.

Lee, I., B. Lehner, T. Vavouri, J. Shin, A. G. Fraser, and E. M. Marcotte. (2010). Predicting genetic modifier loci using functional gene networks. Genome Res 20: 1143-1153: Aug.

Lee, J. H., V. Vittone, E. Diefenbach, A. L. Cunningham, and R. J. Diefenbach. (2008b). Identification of structural protein-protein interactions of herpes simplex virus type 1. Virology 378: 347-354: Sep 1.

Lehner, B., J. I. Semple, S. E. Brown, D. Counsell, R. D. Campbell, and C. M. Sanderson. (2004). Analysis of a high-throughput yeast two-hybrid system and its use to predict the function of intracellular proteins encoded within the human MHC class III region. Genomics 83: 153-167: Jan.

Li, S., C. M. Armstrong, N. Bertin, H. Ge, S. Milstein, M. Boxem, P. O. Vidalain, J. D. Han, A. Chesneau, T. Hao, D. S. Goldberg, N. Li, M. Martinez, J. F. Rual, P. Lamesch, L. Xu, M. Tewari, S. L. Wong, L. V. Zhang, G. F. Berriz, L. Jacotot, P. Vaglio, J. Reboul, T. Hirozane-Kishikawa, Q. Li, H. W. Gabel, A. Elewa, B. Baumgartner, D. J. Rose, H. Yu, S. Bosak, R. Sequerra, A. Fraser, S. E. Mango, W. M. Saxton, S. Strome, S. Van Den Heuvel, F. Piano, J. Vandenhaute, C. Sardet, M. Gerstein, L. Doucette-Stamm, K. C. Gunsalus, J. W. Harper, M. E. Cusick, F. P. Roth, D. E. Hill, and M. Vidal. (2004). A map of the interactome network of the metazoan C. elegans. Science 303: 540-543: Jan 23.

Linghu, B., E. S. Snitkin, Z. Hu, Y. Xia, and C. Delisi. (2009). Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol 10: R91.

Liu, X., and B. Han. (2009). Evolutionary conservation of neighbouring gene pairs in plants. Gene 437: 71-79: May 15.

Lu, L. J., Y. Xia, A. Paccanaro, H. Yu, and M. Gerstein. (2005). Assessing the limits of genomic data integration for predicting protein networks. Genome Res 15: 945-953: Jul.

Ma'ayan, A., S. L. Jenkins, S. Neves, A. Hasseldine, E. Grace, B. Dubin-Thaler, N. J. Eungdamrong, G. Weng, P. T. Ram, J. J. Rice, A. Kershenbaum, G. A. Stolovitzky, R. D. Blitzer, and R. Iyengar. (2005). Formation of regulatory patterns during signal propagation in a Mammalian cellular network. Science 309: 1078-1083: Aug 12. Mangan, S., and U. Alon. (2003). Structure and function of the feed-forward loop network

motif. Proc Natl Acad Sci U S A 100: 11980-11985: Oct 14.

Marcotte, E. M. (2000). Computational genetics: finding protein function by nonhomology methods. Curr Opin Struct Biol 10: 359-365: Jun.

Marcotte, E. M., M. Pellegrini, M. J. Thompson, T. O. Yeates, and D. Eisenberg. (1999a). A combined algorithm for genome-wide prediction of protein function. Nature 402: 83-86: Nov 4.

Marcotte, E. M., M. Pellegrini, H. L. Ng, D. W. Rice, T. O. Yeates, and D. Eisenberg. (1999b). Detecting protein function and protein-protein interactions from genome sequences. Science 285: 751-753: Jul 30.

McGary, K. L., T. J. Park, J. O. Woods, H. J. Cha, J. B. Wallingford, and E. M. Marcotte. (2010). Systematic discovery of nonobvious human disease models through orthologous phenotypes. Proc Natl Acad Sci U S A 107: 6544-6549: Apr 6.

Mika, S., and B. Rost. (2006). Protein-protein interactions more conserved within species than across species. PLoS Comput Biol 2: e79: Jul 21.

Milo, R., S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. (2002). Network motifs: simple building blocks of complex networks. Science 298: 824-827: Oct 25.