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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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Network Medicine in the Age of Biomedical Big Data.

Frontiers in genetics

Sonawane AR, Weiss ST, Glass K, Sharma A.
PMID: 31031797
Front Genet. 2019 Apr 11;10:294. doi: 10.3389/fgene.2019.00294. eCollection 2019.

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity...

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Sonawane AR, Weiss ST, Glass K, et al. Network Medicine in the Age of Biomedical Big Data. Front Genet. 2019;10:294doi: 10.3389/fgene.2019.00294.
Sonawane, A. R., Weiss, S. T., Glass, K., & Sharma, A. (2019). Network Medicine in the Age of Biomedical Big Data. Frontiers in genetics, 10294. https://doi.org/10.3389/fgene.2019.00294
Sonawane, Abhijeet R, et al. "Network Medicine in the Age of Biomedical Big Data." Frontiers in genetics vol. 10 (2019): 294. doi: https://doi.org/10.3389/fgene.2019.00294
Sonawane AR, Weiss ST, Glass K, Sharma A. Network Medicine in the Age of Biomedical Big Data. Front Genet. 2019 Apr 11;10:294. doi: 10.3389/fgene.2019.00294. eCollection 2019. PMID: 31031797; PMCID: PMC6470635.
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Estimating Sample-Specific Regulatory Networks.

iScience

Kuijjer ML, Tung MG, Yuan G, Quackenbush J, Glass K.
PMID: 30981959
iScience. 2019 Apr 26;14:226-240. doi: 10.1016/j.isci.2019.03.021. Epub 2019 Mar 28.

Biological systems are driven by intricate interactions among molecules. Many methods have been developed that draw on large numbers of expression samples to infer connections between genes (or their products). The result is an aggregate network representing a single...

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Kuijjer ML, Tung MG, Yuan G, et al. Estimating Sample-Specific Regulatory Networks. iScience. 2019;14:226-240doi: 10.1016/j.isci.2019.03.021.
Kuijjer, M. L., Tung, M. G., Yuan, G., Quackenbush, J., & Glass, K. (2019). Estimating Sample-Specific Regulatory Networks. iScience, 14226-240. https://doi.org/10.1016/j.isci.2019.03.021
Kuijjer, Marieke Lydia, et al. "Estimating Sample-Specific Regulatory Networks." iScience vol. 14 (2019): 226-240. doi: https://doi.org/10.1016/j.isci.2019.03.021
Kuijjer ML, Tung MG, Yuan G, Quackenbush J, Glass K. Estimating Sample-Specific Regulatory Networks. iScience. 2019 Apr 26;14:226-240. doi: 10.1016/j.isci.2019.03.021. Epub 2019 Mar 28. PMID: 30981959; PMCID: PMC6463816.
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Genomic signatures of the unjamming transition in compressed human bronchial epithelial cells.

Science advances

De Marzio M, Kılıç A, Maiorino E, Mitchel JA, Mwase C, O'Sullivan MJ, McGill M, Chase R, Fredberg JJ, Park JA, Glass K, Weiss ST.
PMID: 34301595
Sci Adv. 2021 Jul 23;7(30). doi: 10.1126/sciadv.abf1088. Print 2021 Jul.

Epithelial tissue can transition from a jammed, solid-like, quiescent phase to an unjammed, fluid-like, migratory phase, but the underlying molecular events of the unjamming transition (UJT) remain largely unexplored. Using primary human bronchial epithelial cells (HBECs) and one well-defined...

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De Marzio M, Kılıç A, Maiorino E, et al. Genomic signatures of the unjamming transition in compressed human bronchial epithelial cells. Sci Adv. 2021;7(30)doi: 10.1126/sciadv.abf1088.
De Marzio, M., Kılıç, A., Maiorino, E., Mitchel, J. A., Mwase, C., O'Sullivan, M. J., McGill, M., Chase, R., Fredberg, J. J., Park, J. A., Glass, K., & Weiss, S. T. (2021). Genomic signatures of the unjamming transition in compressed human bronchial epithelial cells. Science advances, 7(30), . https://doi.org/10.1126/sciadv.abf1088
De Marzio, Margherita, et al. "Genomic signatures of the unjamming transition in compressed human bronchial epithelial cells." Science advances vol. 7,30 (2021). doi: https://doi.org/10.1126/sciadv.abf1088
De Marzio M, Kılıç A, Maiorino E, Mitchel JA, Mwase C, O'Sullivan MJ, McGill M, Chase R, Fredberg JJ, Park JA, Glass K, Weiss ST. Genomic signatures of the unjamming transition in compressed human bronchial epithelial cells. Sci Adv. 2021 Jul 23;7(30). doi: 10.1126/sciadv.abf1088. Print 2021 Jul. PMID: 34301595; PMCID: PMC8302128.
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Constructing gene regulatory networks using epigenetic data.

NPJ systems biology and applications

Sonawane AR, DeMeo DL, Quackenbush J, Glass K.
PMID: 34887443
NPJ Syst Biol Appl. 2021 Dec 09;7(1):45. doi: 10.1038/s41540-021-00208-3.

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing...

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Sonawane AR, DeMeo DL, Quackenbush J, et al. Constructing gene regulatory networks using epigenetic data. NPJ Syst Biol Appl. 2021;7(1):45doi: 10.1038/s41540-021-00208-3.
Sonawane, A. R., DeMeo, D. L., Quackenbush, J., & Glass, K. (2021). Constructing gene regulatory networks using epigenetic data. NPJ systems biology and applications, 7(1), 45. https://doi.org/10.1038/s41540-021-00208-3
Sonawane, Abhijeet Rajendra, et al. "Constructing gene regulatory networks using epigenetic data." NPJ systems biology and applications vol. 7,1 (2021): 45. doi: https://doi.org/10.1038/s41540-021-00208-3
Sonawane AR, DeMeo DL, Quackenbush J, Glass K. Constructing gene regulatory networks using epigenetic data. NPJ Syst Biol Appl. 2021 Dec 09;7(1):45. doi: 10.1038/s41540-021-00208-3. PMID: 34887443; PMCID: PMC8660777.
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Gene regulatory network inference as relaxed graph matching.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence

Weighill D, Guebila MB, Lopes-Ramos C, Glass K, Quackenbush J, Platig J, Burkholz R.
PMID: 34707916
Proc Conf AAAI Artif Intell. 2021 Feb;35(11):10263-10272. Epub 2021 May 18.

Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse...

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Weighill D, Guebila MB, Lopes-Ramos C, et al. Gene regulatory network inference as relaxed graph matching. Proc Conf AAAI Artif Intell. 2021;35(11):10263-10272
Weighill, D., Guebila, M. B., Lopes-Ramos, C., Glass, K., Quackenbush, J., Platig, J., & Burkholz, R. (2021). Gene regulatory network inference as relaxed graph matching. Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 35(11), 10263-10272.
Weighill, Deborah, et al. "Gene regulatory network inference as relaxed graph matching." Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence vol. 35,11 (2021): 10263-10272.
Weighill D, Guebila MB, Lopes-Ramos C, Glass K, Quackenbush J, Platig J, Burkholz R. Gene regulatory network inference as relaxed graph matching. Proc Conf AAAI Artif Intell. 2021 Feb;35(11):10263-10272. Epub 2021 May 18. PMID: 34707916; PMCID: PMC8546743.
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GRAND: a database of gene regulatory network models across human conditions.

Nucleic acids research

Ben Guebila M, Lopes-Ramos CM, Weighill D, Sonawane AR, Burkholz R, Shamsaei B, Platig J, Glass K, Kuijjer ML, Quackenbush J.
PMID: 34508353
Nucleic Acids Res. 2022 Jan 07;50:D610-D621. doi: 10.1093/nar/gkab778.

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps...

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Ben Guebila M, Lopes-Ramos CM, Weighill D, et al. GRAND: a database of gene regulatory network models across human conditions. Nucleic Acids Res. 2022;50:D610-D621doi: 10.1093/nar/gkab778.
Ben Guebila, M., Lopes-Ramos, C. M., Weighill, D., Sonawane, A. R., Burkholz, R., Shamsaei, B., Platig, J., Glass, K., Kuijjer, M. L., & Quackenbush, J. (2022). GRAND: a database of gene regulatory network models across human conditions. Nucleic acids research, 50D610-D621. https://doi.org/10.1093/nar/gkab778
Ben Guebila, Marouen, et al. "GRAND: a database of gene regulatory network models across human conditions." Nucleic acids research vol. 50 (2022): D610-D621. doi: https://doi.org/10.1093/nar/gkab778
Ben Guebila M, Lopes-Ramos CM, Weighill D, Sonawane AR, Burkholz R, Shamsaei B, Platig J, Glass K, Kuijjer ML, Quackenbush J. GRAND: a database of gene regulatory network models across human conditions. Nucleic Acids Res. 2022 Jan 07;50:D610-D621. doi: 10.1093/nar/gkab778. PMID: 34508353; PMCID: PMC8728257.
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Showing 1 to 7 of 7 entries