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Showing 1 to 12 of 44 entries
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Using MEMo to discover mutual exclusivity modules in cancer.

Current protocols in bioinformatics

Ciriello G, Cerami E, Aksoy BA, Sander C, Schultz N.
PMID: 23504936
Curr Protoc Bioinformatics. 2013 Mar;Unit 8.17. doi: 10.1002/0471250953.bi0817s41.

Although individual tumors show surprisingly diverse genomic alterations, these events tend to occur in a limited number of pathways, and alterations that affect the same pathway tend to not co-occur in the same patient. While pathway analysis has been...

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Ciriello G, Cerami E, Aksoy BA, et al. Using MEMo to discover mutual exclusivity modules in cancer. Curr Protoc Bioinformatics. 2013;Unit 8.17doi: 10.1002/0471250953.bi0817s41.
Ciriello, G., Cerami, E., Aksoy, B. A., Sander, C., & Schultz, N. (2013). Using MEMo to discover mutual exclusivity modules in cancer. Current protocols in bioinformatics, Unit 8.17. https://doi.org/10.1002/0471250953.bi0817s41
Ciriello, Giovanni, et al. "Using MEMo to discover mutual exclusivity modules in cancer." Current protocols in bioinformatics vol. (2013): Unit 8.17. doi: https://doi.org/10.1002/0471250953.bi0817s41
Ciriello G, Cerami E, Aksoy BA, Sander C, Schultz N. Using MEMo to discover mutual exclusivity modules in cancer. Curr Protoc Bioinformatics. 2013 Mar;Unit 8.17. doi: 10.1002/0471250953.bi0817s41. PMID: 23504936; PMCID: PMC5563973.
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SpotXplore: a Cytoscape plugin for visual exploration of hotspot expression in gene regulatory networks.

Bioinformatics (Oxford, England)

Westenberg MA, Roerdink JB, Kuipers OP, van Hijum SA.
PMID: 20861033
Bioinformatics. 2010 Nov 15;26(22):2922-3. doi: 10.1093/bioinformatics/btq535. Epub 2010 Sep 21.

UNLABELLED: SpotXplore is a plugin for Cytoscape for extraction and visualization of differentially expressed subnetworks (hotspots) from gene networks. The hotspot-based visualization approach enables interactive exploration of regulatory interactions in differentially expressed gene sets, and it allows a researcher...

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Westenberg MA, Roerdink JB, Kuipers OP, et al. SpotXplore: a Cytoscape plugin for visual exploration of hotspot expression in gene regulatory networks. Bioinformatics. 2010;26(22):2922-3doi: 10.1093/bioinformatics/btq535.
Westenberg, M. A., Roerdink, J. B., Kuipers, O. P., & van Hijum, S. A. (2010). SpotXplore: a Cytoscape plugin for visual exploration of hotspot expression in gene regulatory networks. Bioinformatics (Oxford, England), 26(22), 2922-3. https://doi.org/10.1093/bioinformatics/btq535
Westenberg, Michel A, et al. "SpotXplore: a Cytoscape plugin for visual exploration of hotspot expression in gene regulatory networks." Bioinformatics (Oxford, England) vol. 26,22 (2010): 2922-3. doi: https://doi.org/10.1093/bioinformatics/btq535
Westenberg MA, Roerdink JB, Kuipers OP, van Hijum SA. SpotXplore: a Cytoscape plugin for visual exploration of hotspot expression in gene regulatory networks. Bioinformatics. 2010 Nov 15;26(22):2922-3. doi: 10.1093/bioinformatics/btq535. Epub 2010 Sep 21. PMID: 20861033; PMCID: PMC2971575.
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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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Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease.

Scientific reports

Miao X, Chen X, Xie Z, Lin H.
PMID: 30065343
Sci Rep. 2018 Jul 31;8(1):11492. doi: 10.1038/s41598-018-29904-7.

Coronary artery disease (CAD) is a leading cause of death worldwide. Recent genome-wide association studies have identified more than one hundred susceptibility loci associated with CAD. However, the underlying mechanism of these genetic loci to CAD susceptibility is still...

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Miao X, Chen X, Xie Z, et al. Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease. Sci Rep. 2018;8(1):11492doi: 10.1038/s41598-018-29904-7.
Miao, X., Chen, X., Xie, Z., & Lin, H. (2018). Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease. Scientific reports, 8(1), 11492. https://doi.org/10.1038/s41598-018-29904-7
Miao, Xiao, et al. "Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease." Scientific reports vol. 8,1 (2018): 11492. doi: https://doi.org/10.1038/s41598-018-29904-7
Miao X, Chen X, Xie Z, Lin H. Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease. Sci Rep. 2018 Jul 31;8(1):11492. doi: 10.1038/s41598-018-29904-7. PMID: 30065343; PMCID: PMC6068195.
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Network Analysis of Genome-Wide Association Studies for Chronic Obstructive Pulmonary Disease in the Context of Biological Pathways.

American journal of respiratory and critical care medicine

Mount S, Cirillo E, Stewart K, Coort S, Evelo CT, Wesselius A, Zeegers MP, Schols AMWJ.
PMID: 31343902
Am J Respir Crit Care Med. 2019 Dec 01;200(11):1439-1441. doi: 10.1164/rccm.201904-0902LE.

No abstract available.

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Mount S, Cirillo E, Stewart K, et al. Network Analysis of Genome-Wide Association Studies for Chronic Obstructive Pulmonary Disease in the Context of Biological Pathways. Am J Respir Crit Care Med. 2019;200(11):1439-1441doi: 10.1164/rccm.201904-0902LE.
Mount, S., Cirillo, E., Stewart, K., Coort, S., Evelo, C. T., Wesselius, A., Zeegers, M. P., & Schols, A. M. W. J. (2019). Network Analysis of Genome-Wide Association Studies for Chronic Obstructive Pulmonary Disease in the Context of Biological Pathways. American journal of respiratory and critical care medicine, 200(11), 1439-1441. https://doi.org/10.1164/rccm.201904-0902LE
Mount, Sarah, et al. "Network Analysis of Genome-Wide Association Studies for Chronic Obstructive Pulmonary Disease in the Context of Biological Pathways." American journal of respiratory and critical care medicine vol. 200,11 (2019): 1439-1441. doi: https://doi.org/10.1164/rccm.201904-0902LE
Mount S, Cirillo E, Stewart K, Coort S, Evelo CT, Wesselius A, Zeegers MP, Schols AMWJ. Network Analysis of Genome-Wide Association Studies for Chronic Obstructive Pulmonary Disease in the Context of Biological Pathways. Am J Respir Crit Care Med. 2019 Dec 01;200(11):1439-1441. doi: 10.1164/rccm.201904-0902LE. PMID: 31343902.
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Structural Bistability Analysis of Flower-Shaped and Chain-Shaped Boolean Networks.

IEEE/ACM transactions on computational biology and bioinformatics

Azuma SI, Kure T, Sugie T.
PMID: 31107657
IEEE/ACM Trans Comput Biol Bioinform. 2020 Nov-Dec;17(6):2098-2106. doi: 10.1109/TCBB.2019.2917196. Epub 2020 Dec 08.

Bistability, i.e., the existence of just two stable equilibria, is known to play an important role in biological systems, e.g., cellular differentiation and apoptosis. In this paper, we consider the bistability but as a structural property of a class...

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Azuma SI, Kure T, Sugie T. Structural Bistability Analysis of Flower-Shaped and Chain-Shaped Boolean Networks. IEEE/ACM Trans Comput Biol Bioinform. 2020;17(6):2098-2106doi: 10.1109/TCBB.2019.2917196.
Azuma, S. I., Kure, T., & Sugie, T. (2020). Structural Bistability Analysis of Flower-Shaped and Chain-Shaped Boolean Networks. IEEE/ACM transactions on computational biology and bioinformatics, 17(6), 2098-2106. https://doi.org/10.1109/TCBB.2019.2917196
Azuma, Shun-Ichi, et al. "Structural Bistability Analysis of Flower-Shaped and Chain-Shaped Boolean Networks." IEEE/ACM transactions on computational biology and bioinformatics vol. 17,6 (2020): 2098-2106. doi: https://doi.org/10.1109/TCBB.2019.2917196
Azuma SI, Kure T, Sugie T. Structural Bistability Analysis of Flower-Shaped and Chain-Shaped Boolean Networks. IEEE/ACM Trans Comput Biol Bioinform. 2020 Nov-Dec;17(6):2098-2106. doi: 10.1109/TCBB.2019.2917196. Epub 2020 Dec 08. PMID: 31107657.
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Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis.

Medical science monitor : international medical journal of experimental and clinical research

Li Z, Chyr J, Jia Z, Wang L, Hu X, Wu X, Song C.
PMID: 32888289
Med Sci Monit. 2020 Sep 05;26:e923514. doi: 10.12659/MSM.923514.

BACKGROUND Hypertension is one of the most widespread health conditions in the world, and the molecular mechanism of it is still unclear. In this study, we identified the hub genes (hub miRNA genes) associated with hypertension and explored the...

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Li Z, Chyr J, Jia Z, et al. Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis. Med Sci Monit. 2020;26:e923514doi: 10.12659/MSM.923514.
Li, Z., Chyr, J., Jia, Z., Wang, L., Hu, X., Wu, X., & Song, C. (2020). Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis. Medical science monitor : international medical journal of experimental and clinical research, 26e923514. https://doi.org/10.12659/MSM.923514
Li, Zongjin, et al. "Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis." Medical science monitor : international medical journal of experimental and clinical research vol. 26 (2020): e923514. doi: https://doi.org/10.12659/MSM.923514
Li Z, Chyr J, Jia Z, Wang L, Hu X, Wu X, Song C. Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis. Med Sci Monit. 2020 Sep 05;26:e923514. doi: 10.12659/MSM.923514. PMID: 32888289; PMCID: PMC7491244.
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miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules.

BMC bioinformatics

Zhang J, Liu L, Xu T, Xie Y, Zhao C, Li J, Le TD.
PMID: 31077152
BMC Bioinformatics. 2019 May 10;20(1):235. doi: 10.1186/s12859-019-2861-y.

BACKGROUND: A microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our knowledge of their complex functions remains...

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Zhang J, Liu L, Xu T, et al. miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC Bioinformatics. 2019;20(1):235doi: 10.1186/s12859-019-2861-y.
Zhang, J., Liu, L., Xu, T., Xie, Y., Zhao, C., Li, J., & Le, T. D. (2019). miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC bioinformatics, 20(1), 235. https://doi.org/10.1186/s12859-019-2861-y
Zhang, Junpeng, et al. "miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules." BMC bioinformatics vol. 20,1 (2019): 235. doi: https://doi.org/10.1186/s12859-019-2861-y
Zhang J, Liu L, Xu T, Xie Y, Zhao C, Li J, Le TD. miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC Bioinformatics. 2019 May 10;20(1):235. doi: 10.1186/s12859-019-2861-y. PMID: 31077152; PMCID: PMC6509829.
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Multistability and Bifurcation Analysis of Inhibitory Coupled Cyclic Genetic Regulatory Networks With Delays.

IEEE transactions on nanobioscience

Ling G, Guan ZH, Hu B, Lai Q, Wu Y.
PMID: 28212091
IEEE Trans Nanobioscience. 2017 Apr;16(3):216-225. doi: 10.1109/TNB.2017.2669112. Epub 2017 Feb 14.

Many biological systems have the conspicuous property to present more than one stable state and diverse rhythmic behaviors. A closed relationship between these complex dynamic behaviors and cyclic genetic structures has been witnessed by pioneering works. In this paper,...

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Ling G, Guan ZH, Hu B, et al. Multistability and Bifurcation Analysis of Inhibitory Coupled Cyclic Genetic Regulatory Networks With Delays. IEEE Trans Nanobioscience. 2017;16(3):216-225doi: 10.1109/TNB.2017.2669112.
Ling, G., Guan, Z. H., Hu, B., Lai, Q., & Wu, Y. (2017). Multistability and Bifurcation Analysis of Inhibitory Coupled Cyclic Genetic Regulatory Networks With Delays. IEEE transactions on nanobioscience, 16(3), 216-225. https://doi.org/10.1109/TNB.2017.2669112
Ling, Guang, et al. "Multistability and Bifurcation Analysis of Inhibitory Coupled Cyclic Genetic Regulatory Networks With Delays." IEEE transactions on nanobioscience vol. 16,3 (2017): 216-225. doi: https://doi.org/10.1109/TNB.2017.2669112
Ling G, Guan ZH, Hu B, Lai Q, Wu Y. Multistability and Bifurcation Analysis of Inhibitory Coupled Cyclic Genetic Regulatory Networks With Delays. IEEE Trans Nanobioscience. 2017 Apr;16(3):216-225. doi: 10.1109/TNB.2017.2669112. Epub 2017 Feb 14. PMID: 28212091.
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SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks.

Cell systems

Dibaeinia P, Sinha S.
PMID: 32871105
Cell Syst. 2020 Sep 23;11(3):252-271.e11. doi: 10.1016/j.cels.2020.08.003. Epub 2020 Aug 31.

A common approach to benchmarking of single-cell transcriptomics tools is to generate synthetic datasets that statistically resemble experimental data. However, most existing single-cell simulators do not incorporate transcription factor-gene regulatory interactions that underlie expression dynamics. Here, we present SERGIO,...

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Dibaeinia P, Sinha S. SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks. Cell Syst. 2020;11(3):252-271.e11doi: 10.1016/j.cels.2020.08.003.
Dibaeinia, P., & Sinha, S. (2020). SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks. Cell systems, 11(3), 252-271.e11. https://doi.org/10.1016/j.cels.2020.08.003
Dibaeinia, Payam, and Sinha, Saurabh. "SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks." Cell systems vol. 11,3 (2020): 252-271.e11. doi: https://doi.org/10.1016/j.cels.2020.08.003
Dibaeinia P, Sinha S. SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks. Cell Syst. 2020 Sep 23;11(3):252-271.e11. doi: 10.1016/j.cels.2020.08.003. Epub 2020 Aug 31. PMID: 32871105; PMCID: PMC7530147.
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Directed module detection in a large-scale expression compendium.

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

Fu Q, Lemmens K, Sanchez-Rodriguez A, Thijs IM, Meysman P, Sun H, Fierro AC, Engelen K, Marchal K.
PMID: 22144152
Methods Mol Biol. 2012;804:131-65. doi: 10.1007/978-1-61779-361-5_8.

Public online microarray databases contain tremendous amounts of expression data. Mining these data sources can provide a wealth of information on the underlying transcriptional networks. In this chapter, we illustrate how the web services COLOMBOS and DISTILLER can be...

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Fu Q, Lemmens K, Sanchez-Rodriguez A, et al. Directed module detection in a large-scale expression compendium. Methods Mol Biol. 2012;804:131-65doi: 10.1007/978-1-61779-361-5_8.
Fu, Q., Lemmens, K., Sanchez-Rodriguez, A., Thijs, I. M., Meysman, P., Sun, H., Fierro, A. C., Engelen, K., & Marchal, K. (2012). Directed module detection in a large-scale expression compendium. Methods in molecular biology (Clifton, N.J.), 804131-65. https://doi.org/10.1007/978-1-61779-361-5_8
Fu, Qiang, et al. "Directed module detection in a large-scale expression compendium." Methods in molecular biology (Clifton, N.J.) vol. 804 (2012): 131-65. doi: https://doi.org/10.1007/978-1-61779-361-5_8
Fu Q, Lemmens K, Sanchez-Rodriguez A, Thijs IM, Meysman P, Sun H, Fierro AC, Engelen K, Marchal K. Directed module detection in a large-scale expression compendium. Methods Mol Biol. 2012;804:131-65. doi: 10.1007/978-1-61779-361-5_8. PMID: 22144152.
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Learning gene regulatory networks from next generation sequencing data.

Biometrics

Jia B, Xu S, Xiao G, Lamba V, Liang F.
PMID: 28294287
Biometrics. 2017 Dec;73(4):1221-1230. doi: 10.1111/biom.12682. Epub 2017 Mar 10.

In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data...

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Jia B, Xu S, Xiao G, et al. Learning gene regulatory networks from next generation sequencing data. Biometrics. 2017;73(4):1221-1230doi: 10.1111/biom.12682.
Jia, B., Xu, S., Xiao, G., Lamba, V., & Liang, F. (2017). Learning gene regulatory networks from next generation sequencing data. Biometrics, 73(4), 1221-1230. https://doi.org/10.1111/biom.12682
Jia, Bochao, et al. "Learning gene regulatory networks from next generation sequencing data." Biometrics vol. 73,4 (2017): 1221-1230. doi: https://doi.org/10.1111/biom.12682
Jia B, Xu S, Xiao G, Lamba V, Liang F. Learning gene regulatory networks from next generation sequencing data. Biometrics. 2017 Dec;73(4):1221-1230. doi: 10.1111/biom.12682. Epub 2017 Mar 10. PMID: 28294287; PMCID: PMC6258556.
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Showing 1 to 12 of 44 entries