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Showing 1 to 12 of 61 entries
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Pan-Cancer Analysis of Mutation Hotspots in Protein Domains.

Cell systems

Miller ML, Reznik E, Gauthier NP, Aksoy BA, Korkut A, Gao J, Ciriello G, Schultz N, Sander C.
PMID: 27135912
Cell Syst. 2015 Sep 23;1(3):197-209. doi: 10.1016/j.cels.2015.08.014. Epub 2015 Sep 23.

In cancer genomics, recurrence of mutations in independent tumor samples is a strong indicator of functional impact. However, rare functional mutations can escape detection by recurrence analysis owing to lack of statistical power. We enhance statistical power by extending...

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Miller ML, Reznik E, Gauthier NP, et al. Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell Syst. 2015;1(3):197-209doi: 10.1016/j.cels.2015.08.014.
Miller, M. L., Reznik, E., Gauthier, N. P., Aksoy, B. A., Korkut, A., Gao, J., Ciriello, G., Schultz, N., & Sander, C. (2015). Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell systems, 1(3), 197-209. https://doi.org/10.1016/j.cels.2015.08.014
Miller, Martin L, et al. "Pan-Cancer Analysis of Mutation Hotspots in Protein Domains." Cell systems vol. 1,3 (2015): 197-209. doi: https://doi.org/10.1016/j.cels.2015.08.014
Miller ML, Reznik E, Gauthier NP, Aksoy BA, Korkut A, Gao J, Ciriello G, Schultz N, Sander C. Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell Syst. 2015 Sep 23;1(3):197-209. doi: 10.1016/j.cels.2015.08.014. Epub 2015 Sep 23. PMID: 27135912; PMCID: PMC4982675.
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Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems.

Cell systems

Yu MK, Kramer M, Dutkowski J, Srivas R, Licon K, Kreisberg J, Ng CT, Krogan N, Sharan R, Ideker T.
PMID: 26949740
Cell Syst. 2016 Feb 24;2(2):77-88. doi: 10.1016/j.cels.2016.02.003.

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization...

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Yu MK, Kramer M, Dutkowski J, et al. Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell Syst. 2016;2(2):77-88doi: 10.1016/j.cels.2016.02.003.
Yu, M. K., Kramer, M., Dutkowski, J., Srivas, R., Licon, K., Kreisberg, J., Ng, C. T., Krogan, N., Sharan, R., & Ideker, T. (2016). Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell systems, 2(2), 77-88. https://doi.org/10.1016/j.cels.2016.02.003
Yu, Michael Ku, et al. "Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems." Cell systems vol. 2,2 (2016): 77-88. doi: https://doi.org/10.1016/j.cels.2016.02.003
Yu MK, Kramer M, Dutkowski J, Srivas R, Licon K, Kreisberg J, Ng CT, Krogan N, Sharan R, Ideker T. Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell Syst. 2016 Feb 24;2(2):77-88. doi: 10.1016/j.cels.2016.02.003. PMID: 26949740; PMCID: PMC4772745.
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Cytoscape tools for the web age: D3.js and Cytoscape.js exporters.

F1000Research

Ono K, Demchak B, Ideker T.
PMID: 25520778
F1000Res. 2014 Jul 01;3:143. doi: 10.12688/f1000research.4510.2. eCollection 2014.

In this paper we present new data export modules for Cytoscape 3 that can generate network files for Cytoscape.js and D3.js. Cytoscape.js exporter is implemented as a core feature of Cytoscape 3, and D3.js exporter is available as a...

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Ono K, Demchak B, Ideker T. Cytoscape tools for the web age: D3.js and Cytoscape.js exporters. F1000Res. 2014;3:143doi: 10.12688/f1000research.4510.2.
Ono, K., Demchak, B., & Ideker, T. (2014). Cytoscape tools for the web age: D3.js and Cytoscape.js exporters. F1000Research, 3143. https://doi.org/10.12688/f1000research.4510.2
Ono, Keiichiro, et al. "Cytoscape tools for the web age: D3.js and Cytoscape.js exporters." F1000Research vol. 3 (2014): 143. doi: https://doi.org/10.12688/f1000research.4510.2
Ono K, Demchak B, Ideker T. Cytoscape tools for the web age: D3.js and Cytoscape.js exporters. F1000Res. 2014 Jul 01;3:143. doi: 10.12688/f1000research.4510.2. eCollection 2014. PMID: 25520778; PMCID: PMC4264639.
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Egocentric social network structure, health, and pro-social behaviors in a national panel study of Americans.

PloS one

O'Malley AJ, Arbesman S, Steiger DM, Fowler JH, Christakis NA.
PMID: 22615760
PLoS One. 2012;7(5):e36250. doi: 10.1371/journal.pone.0036250. Epub 2012 May 15.

Using a population-based, panel survey, we study how egocentric social networks change over time, and the relationship between egocentric network properties and health and pro-social behaviors. We find that the number of prosocial activities is strongly positively associated with...

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O'Malley AJ, Arbesman S, Steiger DM, et al. Egocentric social network structure, health, and pro-social behaviors in a national panel study of Americans. PLoS One. 2012;7(5):e36250doi: 10.1371/journal.pone.0036250.
O'Malley, A. J., Arbesman, S., Steiger, D. M., Fowler, J. H., & Christakis, N. A. (2012). Egocentric social network structure, health, and pro-social behaviors in a national panel study of Americans. PloS one, 7(5), e36250. https://doi.org/10.1371/journal.pone.0036250
O'Malley, A James, et al. "Egocentric social network structure, health, and pro-social behaviors in a national panel study of Americans." PloS one vol. 7,5 (2012): e36250. doi: https://doi.org/10.1371/journal.pone.0036250
O'Malley AJ, Arbesman S, Steiger DM, Fowler JH, Christakis NA. Egocentric social network structure, health, and pro-social behaviors in a national panel study of Americans. PLoS One. 2012;7(5):e36250. doi: 10.1371/journal.pone.0036250. Epub 2012 May 15. PMID: 22615760; PMCID: PMC3352911.
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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data.

F1000Research

Diaz-Mejia JJ, Meng EC, Pico AR, MacParland SA, Ketela T, Pugh TJ, Bader GD, Morris JH.
PMID: 31508207
F1000Res. 2019 Mar 15;8. doi: 10.12688/f1000research.18490.2. eCollection 2019.

No abstract available.

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Diaz-Mejia JJ, Meng EC, Pico AR, et al. Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data. F1000Res. 2019;8doi: 10.12688/f1000research.18490.2.
Diaz-Mejia, J. J., Meng, E. C., Pico, A. R., MacParland, S. A., Ketela, T., Pugh, T. J., Bader, G. D., & Morris, J. H. (2019). Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data. F1000Research, 8. https://doi.org/10.12688/f1000research.18490.2
Diaz-Mejia, J Javier, et al. "Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data." F1000Research vol. 8 (2019). doi: https://doi.org/10.12688/f1000research.18490.2
Diaz-Mejia JJ, Meng EC, Pico AR, MacParland SA, Ketela T, Pugh TJ, Bader GD, Morris JH. Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data. F1000Res. 2019 Mar 15;8. doi: 10.12688/f1000research.18490.2. eCollection 2019. PMID: 31508207; PMCID: PMC6720041.
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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data.

F1000Research

Diaz-Mejia JJ, Meng EC, Pico AR, MacParland SA, Ketela T, Pugh TJ, Bader GD, Morris JH.
PMID: 31508207
F1000Res. 2019 Mar 15;8. doi: 10.12688/f1000research.18490.1. eCollection 2019.

No abstract available.

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Diaz-Mejia JJ, Meng EC, Pico AR, et al. Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data. F1000Res. 2019;8doi: 10.12688/f1000research.18490.1.
Diaz-Mejia, J. J., Meng, E. C., Pico, A. R., MacParland, S. A., Ketela, T., Pugh, T. J., Bader, G. D., & Morris, J. H. (2019). Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data. F1000Research, 8. https://doi.org/10.12688/f1000research.18490.1
Diaz-Mejia, J Javier, et al. "Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data." F1000Research vol. 8 (2019). doi: https://doi.org/10.12688/f1000research.18490.1
Diaz-Mejia JJ, Meng EC, Pico AR, MacParland SA, Ketela T, Pugh TJ, Bader GD, Morris JH. Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data. F1000Res. 2019 Mar 15;8. doi: 10.12688/f1000research.18490.1. eCollection 2019. PMID: 31508207; PMCID: PMC6720041.
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A Fast and Flexible Framework for Network-Assisted Genomic Association.

iScience

Carlin DE, Fong SH, Qin Y, Jia T, Huang JK, Bao B, Zhang C, Ideker T.
PMID: 31174177
iScience. 2019 Jun 28;16:155-161. doi: 10.1016/j.isci.2019.05.025. Epub 2019 May 24.

We present an accessible, fast, and customizable network propagation system for pathway boosting and interpretation of genome-wide association studies. This system-NAGA (Network Assisted Genomic Association)-taps the NDEx biological network resource to gain access to thousands of protein networks and...

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Carlin DE, Fong SH, Qin Y, et al. A Fast and Flexible Framework for Network-Assisted Genomic Association. iScience. 2019;16:155-161doi: 10.1016/j.isci.2019.05.025.
Carlin, D. E., Fong, S. H., Qin, Y., Jia, T., Huang, J. K., Bao, B., Zhang, C., & Ideker, T. (2019). A Fast and Flexible Framework for Network-Assisted Genomic Association. iScience, 16155-161. https://doi.org/10.1016/j.isci.2019.05.025
Carlin, Daniel E, et al. "A Fast and Flexible Framework for Network-Assisted Genomic Association." iScience vol. 16 (2019): 155-161. doi: https://doi.org/10.1016/j.isci.2019.05.025
Carlin DE, Fong SH, Qin Y, Jia T, Huang JK, Bao B, Zhang C, Ideker T. A Fast and Flexible Framework for Network-Assisted Genomic Association. iScience. 2019 Jun 28;16:155-161. doi: 10.1016/j.isci.2019.05.025. Epub 2019 May 24. PMID: 31174177; PMCID: PMC6554232.
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netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks.

F1000Research

Pai S, Weber P, Isserlin R, Kaka H, Hui S, Shah MA, Giudice L, Giugno R, Nøhr AK, Baumbach J, Bader GD.
PMID: 33628435
F1000Res. 2020 Oct 15;9:1239. doi: 10.12688/f1000research.26429.2. eCollection 2020.

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx...

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Pai S, Weber P, Isserlin R, et al. netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks. F1000Res. 2020;9:1239doi: 10.12688/f1000research.26429.2.
Pai, S., Weber, P., Isserlin, R., Kaka, H., Hui, S., Shah, M. A., Giudice, L., Giugno, R., Nøhr, A. K., Baumbach, J., & Bader, G. D. (2020). netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks. F1000Research, 91239. https://doi.org/10.12688/f1000research.26429.2
Pai, Shraddha, et al. "netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks." F1000Research vol. 9 (2020): 1239. doi: https://doi.org/10.12688/f1000research.26429.2
Pai S, Weber P, Isserlin R, Kaka H, Hui S, Shah MA, Giudice L, Giugno R, Nøhr AK, Baumbach J, Bader GD. netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks. F1000Res. 2020 Oct 15;9:1239. doi: 10.12688/f1000research.26429.2. eCollection 2020. PMID: 33628435; PMCID: PMC7883323.
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Author-sourced capture of pathway knowledge in computable form using Biofactoid.

eLife

Wong JV, Franz M, Siper MC, Fong D, Durupinar F, Dallago C, Luna A, Giorgi J, Rodchenkov I, Babur Ö, Bachman JA, Gyori BM, Demir E, Bader GD, Sander C.
PMID: 34860157
Elife. 2021 Dec 03;10. doi: 10.7554/eLife.68292.

Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a...

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Wong JV, Franz M, Siper MC, et al. Author-sourced capture of pathway knowledge in computable form using Biofactoid. Elife. 2021;10doi: 10.7554/eLife.68292.
Wong, J. V., Franz, M., Siper, M. C., Fong, D., Durupinar, F., Dallago, C., Luna, A., Giorgi, J., Rodchenkov, I., Babur, �. �., Bachman, J. A., Gyori, B. M., Demir, E., Bader, G. D., & Sander, C. (2021). Author-sourced capture of pathway knowledge in computable form using Biofactoid. eLife, 10. https://doi.org/10.7554/eLife.68292
Wong, Jeffrey V, et al. "Author-sourced capture of pathway knowledge in computable form using Biofactoid." eLife vol. 10 (2021). doi: https://doi.org/10.7554/eLife.68292
Wong JV, Franz M, Siper MC, Fong D, Durupinar F, Dallago C, Luna A, Giorgi J, Rodchenkov I, Babur Ö, Bachman JA, Gyori BM, Demir E, Bader GD, Sander C. Author-sourced capture of pathway knowledge in computable form using Biofactoid. Elife. 2021 Dec 03;10. doi: 10.7554/eLife.68292. PMID: 34860157.
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The Cytoscape Automation app article collection.

F1000Research

Demchak B, Otasek D, Pico AR, Bader GD, Ono K, Settle B, Sage E, Morris JH, Longabaugh W, Lopes C, Kucera M, Treister A, Schwikowski B, Molenaar P, Ideker T.
PMID: 29983926
F1000Res. 2018 Jun 20;7:800. doi: 10.12688/f1000research.15355.1. eCollection 2018.

Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape's CyREST feature has allowed...

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Demchak B, Otasek D, Pico AR, et al. The Cytoscape Automation app article collection. F1000Res. 2018;7:800doi: 10.12688/f1000research.15355.1.
Demchak, B., Otasek, D., Pico, A. R., Bader, G. D., Ono, K., Settle, B., Sage, E., Morris, J. H., Longabaugh, W., Lopes, C., Kucera, M., Treister, A., Schwikowski, B., Molenaar, P., & Ideker, T. (2018). The Cytoscape Automation app article collection. F1000Research, 7800. https://doi.org/10.12688/f1000research.15355.1
Demchak, Barry, et al. "The Cytoscape Automation app article collection." F1000Research vol. 7 (2018): 800. doi: https://doi.org/10.12688/f1000research.15355.1
Demchak B, Otasek D, Pico AR, Bader GD, Ono K, Settle B, Sage E, Morris JH, Longabaugh W, Lopes C, Kucera M, Treister A, Schwikowski B, Molenaar P, Ideker T. The Cytoscape Automation app article collection. F1000Res. 2018 Jun 20;7:800. doi: 10.12688/f1000research.15355.1. eCollection 2018. PMID: 29983926; PMCID: PMC6013757.
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Genetic dissection of complex traits using hierarchical biological knowledge.

PLoS computational biology

Tanaka H, Kreisberg JF, Ideker T.
PMID: 34534210
PLoS Comput Biol. 2021 Sep 17;17(9):e1009373. doi: 10.1371/journal.pcbi.1009373. eCollection 2021 Sep.

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to...

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Tanaka H, Kreisberg JF, Ideker T. Genetic dissection of complex traits using hierarchical biological knowledge. PLoS Comput Biol. 2021;17(9):e1009373doi: 10.1371/journal.pcbi.1009373.
Tanaka, H., Kreisberg, J. F., & Ideker, T. (2021). Genetic dissection of complex traits using hierarchical biological knowledge. PLoS computational biology, 17(9), e1009373. https://doi.org/10.1371/journal.pcbi.1009373
Tanaka, Hidenori, et al. "Genetic dissection of complex traits using hierarchical biological knowledge." PLoS computational biology vol. 17,9 (2021): e1009373. doi: https://doi.org/10.1371/journal.pcbi.1009373
Tanaka H, Kreisberg JF, Ideker T. Genetic dissection of complex traits using hierarchical biological knowledge. PLoS Comput Biol. 2021 Sep 17;17(9):e1009373. doi: 10.1371/journal.pcbi.1009373. eCollection 2021 Sep. PMID: 34534210; PMCID: PMC8480841.
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Molecular Genetics and Economics.

The journal of economic perspectives : a journal of the American Economic Association

Beauchamp JP, Cesarini D, Johannesson M, van der Loos MJ, Koellinger PD, Groenen PJ, Fowler JH, Rosenquist JN, Thurik AR, Christakis NA.
PMID: 22427719
J Econ Perspect. 2011;25(4):57-82. doi: 10.1257/jep.25.4.57.

No abstract available.

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Beauchamp JP, Cesarini D, Johannesson M, et al. Molecular Genetics and Economics. J Econ Perspect. 2011;25(4):57-82doi: 10.1257/jep.25.4.57.
Beauchamp, J. P., Cesarini, D., Johannesson, M., van der Loos, M. J., Koellinger, P. D., Groenen, P. J., Fowler, J. H., Rosenquist, J. N., Thurik, A. R., & Christakis, N. A. (2011). Molecular Genetics and Economics. The journal of economic perspectives : a journal of the American Economic Association, 25(4), 57-82. https://doi.org/10.1257/jep.25.4.57
Beauchamp, Jonathan P, et al. "Molecular Genetics and Economics." The journal of economic perspectives : a journal of the American Economic Association vol. 25,4 (2011): 57-82. doi: https://doi.org/10.1257/jep.25.4.57
Beauchamp JP, Cesarini D, Johannesson M, van der Loos MJ, Koellinger PD, Groenen PJ, Fowler JH, Rosenquist JN, Thurik AR, Christakis NA. Molecular Genetics and Economics. J Econ Perspect. 2011;25(4):57-82. doi: 10.1257/jep.25.4.57. PMID: 22427719; PMCID: PMC3306008.
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Showing 1 to 12 of 61 entries