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A web-based protein interaction network visualizer.

BMC bioinformatics

Salazar GA, Meintjes A, Mazandu GK, Rapanoël HA, Akinola RO, Mulder NJ.
PMID: 24885165
BMC Bioinformatics. 2014 May 06;15:129. doi: 10.1186/1471-2105-15-129.

BACKGROUND: Interaction between proteins is one of the most important mechanisms in the execution of cellular functions. The study of these interactions has provided insight into the functioning of an organism's processes. As of October 2013, Homo sapiens had...

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Salazar GA, Meintjes A, Mazandu GK, et al. A web-based protein interaction network visualizer. BMC Bioinformatics. 2014;15:129doi: 10.1186/1471-2105-15-129.
Salazar, G. A., Meintjes, A., Mazandu, G. K., Rapanoël, H. A., Akinola, R. O., & Mulder, N. J. (2014). A web-based protein interaction network visualizer. BMC bioinformatics, 15129. https://doi.org/10.1186/1471-2105-15-129
Salazar, Gustavo A, et al. "A web-based protein interaction network visualizer." BMC bioinformatics vol. 15 (2014): 129. doi: https://doi.org/10.1186/1471-2105-15-129
Salazar GA, Meintjes A, Mazandu GK, Rapanoël HA, Akinola RO, Mulder NJ. A web-based protein interaction network visualizer. BMC Bioinformatics. 2014 May 06;15:129. doi: 10.1186/1471-2105-15-129. PMID: 24885165; PMCID: PMC4029974.
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Integrative analysis of human omics data using biomolecular networks.

Molecular bioSystems

Robinson JL, Nielsen J.
PMID: 27510223
Mol Biosyst. 2016 Oct 20;12(10):2953-64. doi: 10.1039/c6mb00476h. Epub 2016 Aug 11.

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their...

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Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. Mol Biosyst. 2016;12(10):2953-64doi: 10.1039/c6mb00476h.
Robinson, J. L., & Nielsen, J. (2016). Integrative analysis of human omics data using biomolecular networks. Molecular bioSystems, 12(10), 2953-64. https://doi.org/10.1039/c6mb00476h
Robinson, Jonathan L, and Nielsen, Jens. "Integrative analysis of human omics data using biomolecular networks." Molecular bioSystems vol. 12,10 (2016): 2953-64. doi: https://doi.org/10.1039/c6mb00476h
Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. Mol Biosyst. 2016 Oct 20;12(10):2953-64. doi: 10.1039/c6mb00476h. Epub 2016 Aug 11. PMID: 27510223.
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FunRich: An open access standalone functional enrichment and interaction network analysis tool.

Proteomics

Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CY, Williamson NA, Mouradov D, Sieber OM, Simpson RJ, Salim A, Bacic A, Hill AF, Stroud DA, Ryan MT, Agbinya JI, Mariadason JM, Burgess AW, Mathivanan S.
PMID: 25921073
Proteomics. 2015 Aug;15(15):2597-601. doi: 10.1002/pmic.201400515. Epub 2015 Jun 17.

As high-throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user-friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed...

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Pathan M, Keerthikumar S, Ang CS, et al. FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics. 2015;15(15):2597-601doi: 10.1002/pmic.201400515.
Pathan, M., Keerthikumar, S., Ang, C. S., Gangoda, L., Quek, C. Y., Williamson, N. A., Mouradov, D., Sieber, O. M., Simpson, R. J., Salim, A., Bacic, A., Hill, A. F., Stroud, D. A., Ryan, M. T., Agbinya, J. I., Mariadason, J. M., Burgess, A. W., & Mathivanan, S. (2015). FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics, 15(15), 2597-601. https://doi.org/10.1002/pmic.201400515
Pathan, Mohashin, et al. "FunRich: An open access standalone functional enrichment and interaction network analysis tool." Proteomics vol. 15,15 (2015): 2597-601. doi: https://doi.org/10.1002/pmic.201400515
Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CY, Williamson NA, Mouradov D, Sieber OM, Simpson RJ, Salim A, Bacic A, Hill AF, Stroud DA, Ryan MT, Agbinya JI, Mariadason JM, Burgess AW, Mathivanan S. FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics. 2015 Aug;15(15):2597-601. doi: 10.1002/pmic.201400515. Epub 2015 Jun 17. PMID: 25921073.
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Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms.

Journal of proteome research

Goh WW, Wong L.
PMID: 27454466
J Proteome Res. 2016 Sep 02;15(9):3167-79. doi: 10.1021/acs.jproteome.6b00402. Epub 2016 Aug 10.

Despite advances in proteomic technologies, idiosyncratic data issues, for example, incomplete coverage and inconsistency, resulting in large data holes, persist. Moreover, because of naïve reliance on statistical testing and its accompanying p values, differential protein signatures identified from such...

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Goh WW, Wong L. Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms. J Proteome Res. 2016;15(9):3167-79doi: 10.1021/acs.jproteome.6b00402.
Goh, W. W., & Wong, L. (2016). Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms. Journal of proteome research, 15(9), 3167-79. https://doi.org/10.1021/acs.jproteome.6b00402
Goh, Wilson Wen Bin, and Wong, Limsoon. "Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms." Journal of proteome research vol. 15,9 (2016): 3167-79. doi: https://doi.org/10.1021/acs.jproteome.6b00402
Goh WW, Wong L. Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms. J Proteome Res. 2016 Sep 02;15(9):3167-79. doi: 10.1021/acs.jproteome.6b00402. Epub 2016 Aug 10. PMID: 27454466.
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Estimating gene regulatory networks with pandaR.

Bioinformatics (Oxford, England)

Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J.
PMID: 28334344
Bioinformatics. 2017 Jul 15;33(14):2232-2234. doi: 10.1093/bioinformatics/btx139.

CONTACT: [email protected] or [email protected] AND IMPLEMENTATION: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.

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Schlauch D, Paulson JN, Young A, et al. Estimating gene regulatory networks with pandaR. Bioinformatics. 2017;33(14):2232-2234doi: 10.1093/bioinformatics/btx139.
Schlauch, D., Paulson, J. N., Young, A., Glass, K., & Quackenbush, J. (2017). Estimating gene regulatory networks with pandaR. Bioinformatics (Oxford, England), 33(14), 2232-2234. https://doi.org/10.1093/bioinformatics/btx139
Schlauch, Daniel, et al. "Estimating gene regulatory networks with pandaR." Bioinformatics (Oxford, England) vol. 33,14 (2017): 2232-2234. doi: https://doi.org/10.1093/bioinformatics/btx139
Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J. Estimating gene regulatory networks with pandaR. Bioinformatics. 2017 Jul 15;33(14):2232-2234. doi: 10.1093/bioinformatics/btx139. PMID: 28334344; PMCID: PMC5870629.
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OntoPIN: an ontology-annotated PPI database.

Interdisciplinary sciences, computational life sciences

Guzzi PH, Veltri P, Cannataro M.
PMID: 24307410
Interdiscip Sci. 2013 Sep;5(3):187-95. doi: 10.1007/s12539-013-0173-x. Epub 2013 Dec 04.

Protein-protein interaction (PPI) data stored in publicly available databases are queried by the use of simple query interfaces allowing only key-based queries. A typical query on such databases is based on the use of protein identifiers and enables the...

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Guzzi PH, Veltri P, Cannataro M. OntoPIN: an ontology-annotated PPI database. Interdiscip Sci. 2013;5(3):187-95doi: 10.1007/s12539-013-0173-x.
Guzzi, P. H., Veltri, P., & Cannataro, M. (2013). OntoPIN: an ontology-annotated PPI database. Interdisciplinary sciences, computational life sciences, 5(3), 187-95. https://doi.org/10.1007/s12539-013-0173-x
Guzzi, Pietro Hiram, et al. "OntoPIN: an ontology-annotated PPI database." Interdisciplinary sciences, computational life sciences vol. 5,3 (2013): 187-95. doi: https://doi.org/10.1007/s12539-013-0173-x
Guzzi PH, Veltri P, Cannataro M. OntoPIN: an ontology-annotated PPI database. Interdiscip Sci. 2013 Sep;5(3):187-95. doi: 10.1007/s12539-013-0173-x. Epub 2013 Dec 04. PMID: 24307410.
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Differentiating disease subtypes by using pathway patterns constructed from gene expressions and protein networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Hung FH, Chiu HW.
PMID: 26737786
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6519-22. doi: 10.1109/EMBC.2015.7319886.

Gene expression profiles differ in different diseases. Even if diseases are at the same stage, such diseases exhibit different gene expressions, not to mention the different subtypes at a single lesion site. Distinguishing different disease subtypes at a single...

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Hung FH, Chiu HW. Differentiating disease subtypes by using pathway patterns constructed from gene expressions and protein networks. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6519-22doi: 10.1109/EMBC.2015.7319886.
Hung, F. H., & Chiu, H. W. (2015). Differentiating disease subtypes by using pathway patterns constructed from gene expressions and protein networks. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 20156519-22. https://doi.org/10.1109/EMBC.2015.7319886
Hung, Fei-Hung, and Chiu, Hung-Wen. "Differentiating disease subtypes by using pathway patterns constructed from gene expressions and protein networks." Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference vol. 2015 (2015): 6519-22. doi: https://doi.org/10.1109/EMBC.2015.7319886
Hung FH, Chiu HW. Differentiating disease subtypes by using pathway patterns constructed from gene expressions and protein networks. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6519-22. doi: 10.1109/EMBC.2015.7319886. PMID: 26737786.
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Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation.

Proteomics

Yin X, Liu X, Zhang Y, Yan G, Wang F, Lu H, Shen H, Yang P.
PMID: 25044409
Proteomics. 2014 Sep;14(17):2008-16. doi: 10.1002/pmic.201300510. Epub 2014 Aug 13.

In this paper, we demonstrate a rapid and reproducible 1D LC-MS/MS workflow for fast quantitative proteomic research. We have optimized the LC-MS/MS conditions, including digestion and gradient conditions, sample loading amount, and MS parameter settings. As a result, we...

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Yin X, Liu X, Zhang Y, et al. Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation. Proteomics. 2014;14(17):2008-16doi: 10.1002/pmic.201300510.
Yin, X., Liu, X., Zhang, Y., Yan, G., Wang, F., Lu, H., Shen, H., & Yang, P. (2014). Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation. Proteomics, 14(17), 2008-16. https://doi.org/10.1002/pmic.201300510
Yin, Xuefei, et al. "Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation." Proteomics vol. 14,17 (2014): 2008-16. doi: https://doi.org/10.1002/pmic.201300510
Yin X, Liu X, Zhang Y, Yan G, Wang F, Lu H, Shen H, Yang P. Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation. Proteomics. 2014 Sep;14(17):2008-16. doi: 10.1002/pmic.201300510. Epub 2014 Aug 13. PMID: 25044409.
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Displaying chemical information on a biological network using Cytoscape.

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

Wallace IM, Bader GD, Giaever G, Nislow C.
PMID: 21877291
Methods Mol Biol. 2011;781:363-76. doi: 10.1007/978-1-61779-276-2_18.

Cytoscape is an open-source software package that is widely used to integrate and visualize diverse data sets in biology. This chapter explains how to use Cytoscape to integrate open-source chemical information with a biological network. By visualizing information about...

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Wallace IM, Bader GD, Giaever G, et al. Displaying chemical information on a biological network using Cytoscape. Methods Mol Biol. 2011;781:363-76doi: 10.1007/978-1-61779-276-2_18.
Wallace, I. M., Bader, G. D., Giaever, G., & Nislow, C. (2011). Displaying chemical information on a biological network using Cytoscape. Methods in molecular biology (Clifton, N.J.), 781363-76. https://doi.org/10.1007/978-1-61779-276-2_18
Wallace, Iain M, et al. "Displaying chemical information on a biological network using Cytoscape." Methods in molecular biology (Clifton, N.J.) vol. 781 (2011): 363-76. doi: https://doi.org/10.1007/978-1-61779-276-2_18
Wallace IM, Bader GD, Giaever G, Nislow C. Displaying chemical information on a biological network using Cytoscape. Methods Mol Biol. 2011;781:363-76. doi: 10.1007/978-1-61779-276-2_18. PMID: 21877291.
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Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis.

International ophthalmology

Sun H, Cheng Y, Yan Z, Liu X, Zhang J.
PMID: 31953631
Int Ophthalmol. 2020 Feb;40(2):269-279. doi: 10.1007/s10792-019-01158-w. Epub 2020 Jan 17.

PURPOSE: Diabetic retinopathy (DR) especially proliferative diabetic retinopathy (PDR) is a serious eye disease. We aimed to identify key pathway and hub genes associated with PDR by analyzing the expression of retinal fibrovascular tissue in PDR patients.METHODS: First raw...

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Sun H, Cheng Y, Yan Z, et al. Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. Int Ophthalmol. 2020;40(2):269-279doi: 10.1007/s10792-019-01158-w.
Sun, H., Cheng, Y., Yan, Z., Liu, X., & Zhang, J. (2020). Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. International ophthalmology, 40(2), 269-279. https://doi.org/10.1007/s10792-019-01158-w
Sun, Haiyan, et al. "Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis." International ophthalmology vol. 40,2 (2020): 269-279. doi: https://doi.org/10.1007/s10792-019-01158-w
Sun H, Cheng Y, Yan Z, Liu X, Zhang J. Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. Int Ophthalmol. 2020 Feb;40(2):269-279. doi: 10.1007/s10792-019-01158-w. Epub 2020 Jan 17. PMID: 31953631.
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uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes.

Cell systems

Hristov BH, Chazelle B, Singh M.
PMID: 32684276
Cell Syst. 2020 Jun 24;10(6):470-479.e3. doi: 10.1016/j.cels.2020.05.008. Epub 2020 Jun 24.

Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that...

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Hristov BH, Chazelle B, Singh M. uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes. Cell Syst. 2020;10(6):470-479.e3doi: 10.1016/j.cels.2020.05.008.
Hristov, B. H., Chazelle, B., & Singh, M. (2020). uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes. Cell systems, 10(6), 470-479.e3. https://doi.org/10.1016/j.cels.2020.05.008
Hristov, Borislav H, et al. "uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes." Cell systems vol. 10,6 (2020): 470-479.e3. doi: https://doi.org/10.1016/j.cels.2020.05.008
Hristov BH, Chazelle B, Singh M. uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes. Cell Syst. 2020 Jun 24;10(6):470-479.e3. doi: 10.1016/j.cels.2020.05.008. Epub 2020 Jun 24. PMID: 32684276; PMCID: PMC7821437.
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A systematic approach to orient the human protein-protein interaction network.

Nature communications

Silverbush D, Sharan R.
PMID: 31289271
Nat Commun. 2019 Jul 09;10(1):3015. doi: 10.1038/s41467-019-10887-6.

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in...

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Silverbush D, Sharan R. A systematic approach to orient the human protein-protein interaction network. Nat Commun. 2019;10(1):3015doi: 10.1038/s41467-019-10887-6.
Silverbush, D., & Sharan, R. (2019). A systematic approach to orient the human protein-protein interaction network. Nature communications, 10(1), 3015. https://doi.org/10.1038/s41467-019-10887-6
Silverbush, Dana, and Sharan, Roded. "A systematic approach to orient the human protein-protein interaction network." Nature communications vol. 10,1 (2019): 3015. doi: https://doi.org/10.1038/s41467-019-10887-6
Silverbush D, Sharan R. A systematic approach to orient the human protein-protein interaction network. Nat Commun. 2019 Jul 09;10(1):3015. doi: 10.1038/s41467-019-10887-6. PMID: 31289271; PMCID: PMC6617457.
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Showing 1 to 12 of 110 entries