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Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

TheScientificWorldJournal

Yang L, Tang X.
PMID: 24578640
ScientificWorldJournal. 2014 Jan 22;2014:523634. doi: 10.1155/2014/523634. eCollection 2014.

Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend...

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Yang L, Tang X. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering. ScientificWorldJournal. 2014;2014:523634doi: 10.1155/2014/523634.
Yang, L., & Tang, X. (2014). Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering. TheScientificWorldJournal, 2014523634. https://doi.org/10.1155/2014/523634
Yang, Lei, and Tang, Xianglong. "Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering." TheScientificWorldJournal vol. 2014 (2014): 523634. doi: https://doi.org/10.1155/2014/523634
Yang L, Tang X. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering. ScientificWorldJournal. 2014 Jan 22;2014:523634. doi: 10.1155/2014/523634. eCollection 2014. PMID: 24578640; PMCID: PMC3919085.
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Using the PRIDE proteomics identifications database for knowledge discovery and data analysis.

Methods in molecular biology (Clifton, N.J.)

Jones P, Martens L.
PMID: 20013379
Methods Mol Biol. 2010;604:297-307. doi: 10.1007/978-1-60761-444-9_20.

The PRIDE Proteomics Identifications Database provides users with the ability to explore and compare mass spectrometry-based proteomics experiments that reveal details of the protein expression found in a broad range of taxonomic groups, tissues and disease states. A PRIDE...

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Jones P, Martens L. Using the PRIDE proteomics identifications database for knowledge discovery and data analysis. Methods Mol Biol. 2010;604:297-307doi: 10.1007/978-1-60761-444-9_20.
Jones, P., & Martens, L. (2010). Using the PRIDE proteomics identifications database for knowledge discovery and data analysis. Methods in molecular biology (Clifton, N.J.), 604297-307. https://doi.org/10.1007/978-1-60761-444-9_20
Jones, Philip, and Martens, Lennart. "Using the PRIDE proteomics identifications database for knowledge discovery and data analysis." Methods in molecular biology (Clifton, N.J.) vol. 604 (2010): 297-307. doi: https://doi.org/10.1007/978-1-60761-444-9_20
Jones P, Martens L. Using the PRIDE proteomics identifications database for knowledge discovery and data analysis. Methods Mol Biol. 2010;604:297-307. doi: 10.1007/978-1-60761-444-9_20. PMID: 20013379.
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UniHI 4: new tools for query, analysis and visualization of the human protein-protein interactome.

Nucleic acids research

Chaurasia G, Malhotra S, Russ J, Schnoegl S, Hänig C, Wanker EE, Futschik ME.
PMID: 18984619
Nucleic Acids Res. 2009 Jan;37:D657-60. doi: 10.1093/nar/gkn841. Epub 2008 Nov 04.

Human protein interaction maps have become important tools of biomedical research for the elucidation of molecular mechanisms and the identification of new modulators of disease processes. The Unified Human Interactome database (UniHI, http://www.unihi.org) provides researchers with a comprehensive platform...

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Chaurasia G, Malhotra S, Russ J, et al. UniHI 4: new tools for query, analysis and visualization of the human protein-protein interactome. Nucleic Acids Res. 2008;37:D657-60doi: 10.1093/nar/gkn841.
Chaurasia, G., Malhotra, S., Russ, J., Schnoegl, S., Hänig, C., Wanker, E. E., & Futschik, M. E. (2009). UniHI 4: new tools for query, analysis and visualization of the human protein-protein interactome. Nucleic acids research, 37D657-60. https://doi.org/10.1093/nar/gkn841
Chaurasia, Gautam, et al. "UniHI 4: new tools for query, analysis and visualization of the human protein-protein interactome." Nucleic acids research vol. 37 (2009): D657-60. doi: https://doi.org/10.1093/nar/gkn841
Chaurasia G, Malhotra S, Russ J, Schnoegl S, Hänig C, Wanker EE, Futschik ME. UniHI 4: new tools for query, analysis and visualization of the human protein-protein interactome. Nucleic Acids Res. 2009 Jan;37:D657-60. doi: 10.1093/nar/gkn841. Epub 2008 Nov 04. PMID: 18984619; PMCID: PMC2686569.
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Clinical protein metabolism.

Master schedule of professional staff proceedings ... United States. Army. Halloran general hospital, St. George, Staten island, N. Y

CO TUI.
PMID: 20246934
Master Sched Prof Staff Proc. 1946;1(2):15-8.

No abstract available.

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CO TUI. Clinical protein metabolism. Master Sched Prof Staff Proc. 1946;1(2):15-8
CO TUI. (1946). Clinical protein metabolism. Master schedule of professional staff proceedings ... United States. Army. Halloran general hospital, St. George, Staten island, N. Y, 1(2), 15-8.
CO TUI. "Clinical protein metabolism." Master schedule of professional staff proceedings ... United States. Army. Halloran general hospital, St. George, Staten island, N. Y vol. 1,2 (1946): 15-8.
CO TUI. Clinical protein metabolism. Master Sched Prof Staff Proc. 1946;1(2):15-8. PMID: 20246934.
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Bioinformatics tools for the functional interpretation of quantitative proteomics results.

Current topics in medicinal chemistry

Villavicencio-Diaz TN, Rodriguez-Ulloa A, Guirola-Cruz O, Perez-Riverol Y.
PMID: 24304321
Curr Top Med Chem. 2014;14(3):435-49. doi: 10.2174/1568026613666131204105110.

Proteins are the principal mediators of the functions in the cell; therefore, any abnormal variations on their abundance levels may reflect the presence of pathological processes. In this sense, many researchers rely on the functional interpretation of protein lists...

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Villavicencio-Diaz TN, Rodriguez-Ulloa A, Guirola-Cruz O, et al. Bioinformatics tools for the functional interpretation of quantitative proteomics results. Curr Top Med Chem. 2014;14(3):435-49doi: 10.2174/1568026613666131204105110.
Villavicencio-Diaz, T. N., Rodriguez-Ulloa, A., Guirola-Cruz, O., & Perez-Riverol, Y. (2014). Bioinformatics tools for the functional interpretation of quantitative proteomics results. Current topics in medicinal chemistry, 14(3), 435-49. https://doi.org/10.2174/1568026613666131204105110
Villavicencio-Diaz, Teresa Nunez, et al. "Bioinformatics tools for the functional interpretation of quantitative proteomics results." Current topics in medicinal chemistry vol. 14,3 (2014): 435-49. doi: https://doi.org/10.2174/1568026613666131204105110
Villavicencio-Diaz TN, Rodriguez-Ulloa A, Guirola-Cruz O, Perez-Riverol Y. Bioinformatics tools for the functional interpretation of quantitative proteomics results. Curr Top Med Chem. 2014;14(3):435-49. doi: 10.2174/1568026613666131204105110. PMID: 24304321.
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Editorial overview: Catalysis and regulation.

Current opinion in structural biology

Klinman JP, Rosenzweig AC.
PMID: 26573393
Curr Opin Struct Biol. 2015 Dec;35:iv-vi. doi: 10.1016/j.sbi.2015.10.003. Epub 2015 Nov 10.

No abstract available.

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Klinman JP, Rosenzweig AC. Editorial overview: Catalysis and regulation. Curr Opin Struct Biol. 2015;35:iv-vidoi: 10.1016/j.sbi.2015.10.003.
Klinman, J. P., & Rosenzweig, A. C. (2015). Editorial overview: Catalysis and regulation. Current opinion in structural biology, 35iv-vi. https://doi.org/10.1016/j.sbi.2015.10.003
Klinman, Judith P, and Rosenzweig, Amy C. "Editorial overview: Catalysis and regulation." Current opinion in structural biology vol. 35 (2015): iv-vi. doi: https://doi.org/10.1016/j.sbi.2015.10.003
Klinman JP, Rosenzweig AC. Editorial overview: Catalysis and regulation. Curr Opin Struct Biol. 2015 Dec;35:iv-vi. doi: 10.1016/j.sbi.2015.10.003. Epub 2015 Nov 10. PMID: 26573393.
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The Rise of Mass Spectrometry and the Fall of Edman Degradation.

Clinical chemistry

Mann M.
PMID: 26430071
Clin Chem. 2016 Jan;62(1):293-4. doi: 10.1373/clinchem.2014.237271. Epub 2015 Oct 01.

No abstract available.

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Mann M. The Rise of Mass Spectrometry and the Fall of Edman Degradation. Clin Chem. 2015;62(1):293-4doi: 10.1373/clinchem.2014.237271.
Mann, M. (2016). The Rise of Mass Spectrometry and the Fall of Edman Degradation. Clinical chemistry, 62(1), 293-4. https://doi.org/10.1373/clinchem.2014.237271
Mann, Matthias. "The Rise of Mass Spectrometry and the Fall of Edman Degradation." Clinical chemistry vol. 62,1 (2016): 293-4. doi: https://doi.org/10.1373/clinchem.2014.237271
Mann M. The Rise of Mass Spectrometry and the Fall of Edman Degradation. Clin Chem. 2016 Jan;62(1):293-4. doi: 10.1373/clinchem.2014.237271. Epub 2015 Oct 01. PMID: 26430071.
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The accuracy of telling time via oscillatory signals.

Physical biology

Monti M, Wolde PR.
PMID: 27203353
Phys Biol. 2016 May 20;13(3):035005. doi: 10.1088/1478-3975/13/3/035005.

Circadian clocks are the central timekeepers of life, allowing cells to anticipate changes between day and night. Experiments in recent years have revealed that circadian clocks can be highly stable, raising the question how reliably they can be read...

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Monti M, Wolde PR. The accuracy of telling time via oscillatory signals. Phys Biol. 2016;13(3):035005doi: 10.1088/1478-3975/13/3/035005.
Monti, M., & Wolde, P. R. (2016). The accuracy of telling time via oscillatory signals. Physical biology, 13(3), 035005. https://doi.org/10.1088/1478-3975/13/3/035005
Monti, Michele, and Wolde, Pieter Rein Ten. "The accuracy of telling time via oscillatory signals." Physical biology vol. 13,3 (2016): 035005. doi: https://doi.org/10.1088/1478-3975/13/3/035005
Monti M, Wolde PR. The accuracy of telling time via oscillatory signals. Phys Biol. 2016 May 20;13(3):035005. doi: 10.1088/1478-3975/13/3/035005. PMID: 27203353.
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Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction.

BMC bioinformatics

Mora A, Donaldson IM.
PMID: 23146171
BMC Bioinformatics. 2012 Nov 12;13:294. doi: 10.1186/1471-2105-13-294.

BACKGROUND: Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes,...

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Mora A, Donaldson IM. Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction. BMC Bioinformatics. 2012;13:294doi: 10.1186/1471-2105-13-294.
Mora, A., & Donaldson, I. M. (2012). Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction. BMC bioinformatics, 13294. https://doi.org/10.1186/1471-2105-13-294
Mora, Antonio, and Donaldson, Ian M. "Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction." BMC bioinformatics vol. 13 (2012): 294. doi: https://doi.org/10.1186/1471-2105-13-294
Mora A, Donaldson IM. Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction. BMC Bioinformatics. 2012 Nov 12;13:294. doi: 10.1186/1471-2105-13-294. PMID: 23146171; PMCID: PMC3534413.
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Prediction of a missing protein expression map in the context of the human proteome project.

Journal of proteome research

Guruceaga E, Sanchez del Pino MM, Corrales FJ, Segura V.
PMID: 25612097
J Proteome Res. 2015 Mar 06;14(3):1350-60. doi: 10.1021/pr500850u. Epub 2015 Feb 05.

Experimental evidence for the entire human proteome has been defined in the Human Proteome Project, and it is publicly available in the neXtProt database. However, there are still human proteins for which reliable experimental evidence does not exist, and...

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Guruceaga E, Sanchez del Pino MM, Corrales FJ, et al. Prediction of a missing protein expression map in the context of the human proteome project. J Proteome Res. 2015;14(3):1350-60doi: 10.1021/pr500850u.
Guruceaga, E., Sanchez del Pino, M. M., Corrales, F. J., & Segura, V. (2015). Prediction of a missing protein expression map in the context of the human proteome project. Journal of proteome research, 14(3), 1350-60. https://doi.org/10.1021/pr500850u
Guruceaga, Elizabeth, et al. "Prediction of a missing protein expression map in the context of the human proteome project." Journal of proteome research vol. 14,3 (2015): 1350-60. doi: https://doi.org/10.1021/pr500850u
Guruceaga E, Sanchez del Pino MM, Corrales FJ, Segura V. Prediction of a missing protein expression map in the context of the human proteome project. J Proteome Res. 2015 Mar 06;14(3):1350-60. doi: 10.1021/pr500850u. Epub 2015 Feb 05. PMID: 25612097.
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Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.

BMC bioinformatics

Wan S, Mak MW, Kung SY.
PMID: 26911432
BMC Bioinformatics. 2016 Feb 24;17:97. doi: 10.1186/s12859-016-0940-x.

BACKGROUND: Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO)...

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Wan S, Mak MW, Kung SY. Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins. BMC Bioinformatics. 2016;17:97doi: 10.1186/s12859-016-0940-x.
Wan, S., Mak, M. W., & Kung, S. Y. (2016). Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins. BMC bioinformatics, 1797. https://doi.org/10.1186/s12859-016-0940-x
Wan, Shibiao, et al. "Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins." BMC bioinformatics vol. 17 (2016): 97. doi: https://doi.org/10.1186/s12859-016-0940-x
Wan S, Mak MW, Kung SY. Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins. BMC Bioinformatics. 2016 Feb 24;17:97. doi: 10.1186/s12859-016-0940-x. PMID: 26911432; PMCID: PMC4765148.
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Classifying ten types of major cancers based on reverse phase protein array profiles.

PloS one

Zhang PW, Chen L, Huang T, Zhang N, Kong XY, Cai YD.
PMID: 25822500
PLoS One. 2015 Mar 30;10(3):e0123147. doi: 10.1371/journal.pone.0123147. eCollection 2015.

Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose...

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Zhang PW, Chen L, Huang T, et al. Classifying ten types of major cancers based on reverse phase protein array profiles. PLoS One. 2015;10(3):e0123147doi: 10.1371/journal.pone.0123147.
Zhang, P. W., Chen, L., Huang, T., Zhang, N., Kong, X. Y., & Cai, Y. D. (2015). Classifying ten types of major cancers based on reverse phase protein array profiles. PloS one, 10(3), e0123147. https://doi.org/10.1371/journal.pone.0123147
Zhang, Pei-Wei, et al. "Classifying ten types of major cancers based on reverse phase protein array profiles." PloS one vol. 10,3 (2015): e0123147. doi: https://doi.org/10.1371/journal.pone.0123147
Zhang PW, Chen L, Huang T, Zhang N, Kong XY, Cai YD. Classifying ten types of major cancers based on reverse phase protein array profiles. PLoS One. 2015 Mar 30;10(3):e0123147. doi: 10.1371/journal.pone.0123147. eCollection 2015. PMID: 25822500; PMCID: PMC4378934.
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Showing 13 to 24 of 41 entries