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Showing 1 to 12 of 21 entries
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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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Mapping the multiscale structure of biological systems.

Cell systems

Schaffer LV, Ideker T.
PMID: 34139169
Cell Syst. 2021 Jun 16;12(6):622-635. doi: 10.1016/j.cels.2021.05.012.

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological...

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Schaffer LV, Ideker T. Mapping the multiscale structure of biological systems. Cell Syst. 2021;12(6):622-635doi: 10.1016/j.cels.2021.05.012.
Schaffer, L. V., & Ideker, T. (2021). Mapping the multiscale structure of biological systems. Cell systems, 12(6), 622-635. https://doi.org/10.1016/j.cels.2021.05.012
Schaffer, Leah V, and Ideker, Trey. "Mapping the multiscale structure of biological systems." Cell systems vol. 12,6 (2021): 622-635. doi: https://doi.org/10.1016/j.cels.2021.05.012
Schaffer LV, Ideker T. Mapping the multiscale structure of biological systems. Cell Syst. 2021 Jun 16;12(6):622-635. doi: 10.1016/j.cels.2021.05.012. PMID: 34139169; PMCID: PMC8245186.
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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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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.26429.1. 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...

Cite
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.26429.1.
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.26429.1
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.26429.1
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.26429.1. eCollection 2020. PMID: 33628435; PMCID: PMC7883323.
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Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.

Nature cancer

Ma J, Fong SH, Luo Y, Bakkenist CJ, Shen JP, Mourragui S, Wessels LFA, Hafner M, Sharan R, Peng J, Ideker T.
PMID: 34223192
Nat Cancer. 2021 Feb;2(2):233-244. doi: 10.1038/s43018-020-00169-2. Epub 2021 Jan 25.

Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed technique,...

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Ma J, Fong SH, Luo Y, et al. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat Cancer. 2021;2(2):233-244doi: 10.1038/s43018-020-00169-2.
Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S., Wessels, L. F. A., Hafner, M., Sharan, R., Peng, J., & Ideker, T. (2021). Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nature cancer, 2(2), 233-244. https://doi.org/10.1038/s43018-020-00169-2
Ma, Jianzhu, et al. "Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients." Nature cancer vol. 2,2 (2021): 233-244. doi: https://doi.org/10.1038/s43018-020-00169-2
Ma J, Fong SH, Luo Y, Bakkenist CJ, Shen JP, Mourragui S, Wessels LFA, Hafner M, Sharan R, Peng J, Ideker T. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat Cancer. 2021 Feb;2(2):233-244. doi: 10.1038/s43018-020-00169-2. Epub 2021 Jan 25. PMID: 34223192; PMCID: PMC8248912.
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AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations.

F1000Research

Kucera M, Isserlin R, Arkhangorodsky A, Bader GD.
PMID: 27830058
F1000Res. 2016 Jul 15;5:1717. doi: 10.12688/f1000research.9090.1. eCollection 2016.

Networks often contain regions of tightly connected nodes, or clusters, that highlight their shared relationships. An effective way to create a visual summary of a network is to identify clusters and annotate them with an enclosing shape and a...

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Kucera M, Isserlin R, Arkhangorodsky A, et al. AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations. F1000Res. 2016;5:1717doi: 10.12688/f1000research.9090.1.
Kucera, M., Isserlin, R., Arkhangorodsky, A., & Bader, G. D. (2016). AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations. F1000Research, 51717. https://doi.org/10.12688/f1000research.9090.1
Kucera, Mike, et al. "AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations." F1000Research vol. 5 (2016): 1717. doi: https://doi.org/10.12688/f1000research.9090.1
Kucera M, Isserlin R, Arkhangorodsky A, Bader GD. AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations. F1000Res. 2016 Jul 15;5:1717. doi: 10.12688/f1000research.9090.1. eCollection 2016. PMID: 27830058; PMCID: PMC5082607.
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Strategies for Network GWAS Evaluated Using Classroom Crowd Science.

Cell systems

Fong SH, Carlin DE, Ozturk K, Ideker T.
PMID: 31022372
Cell Syst. 2019 Apr 24;8(4):275-280. doi: 10.1016/j.cels.2019.03.013.

Biological networks can substantially boost power to identify disease genes in genome-wide association studies. To explore different network GWAS methods, we challenged students of a UC San Diego graduate level bioinformatics course, Network Biology and Biomedicine, to explore and...

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Fong SH, Carlin DE, Ozturk K. Strategies for Network GWAS Evaluated Using Classroom Crowd Science. Cell Syst. 2019;8(4):275-280doi: 10.1016/j.cels.2019.03.013.
Fong, S. H., Carlin, D. E., Ozturk, K., & Ideker, T. (2019). Strategies for Network GWAS Evaluated Using Classroom Crowd Science. Cell systems, 8(4), 275-280. https://doi.org/10.1016/j.cels.2019.03.013
Fong, Samson H, et al. "Strategies for Network GWAS Evaluated Using Classroom Crowd Science." Cell systems vol. 8,4 (2019): 275-280. doi: https://doi.org/10.1016/j.cels.2019.03.013
Fong SH, Carlin DE, Ozturk K, Ideker T. Strategies for Network GWAS Evaluated Using Classroom Crowd Science. Cell Syst. 2019 Apr 24;8(4):275-280. doi: 10.1016/j.cels.2019.03.013. PMID: 31022372; PMCID: PMC6764759.
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Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis.

iScience

Moutaoufik MT, Malty R, Amin S, Zhang Q, Phanse S, Gagarinova A, Zilocchi M, Hoell L, Minic Z, Gagarinova M, Aoki H, Stockwell J, Jessulat M, Goebels F, Broderick K, Scott NE, Vlasblom J, Musso G, Prasad B, Lamantea E, Garavaglia B, Rajput A, Murayama K, Okazaki Y, Foster LJ, Bader GD, Cayabyab FS, Babu M.
PMID: 31536960
iScience. 2019 Sep 27;19:1114-1132. doi: 10.1016/j.isci.2019.08.057. Epub 2019 Sep 04.

Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps...

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Moutaoufik MT, Malty R, Amin S, et al. Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience. 2019;19:1114-1132doi: 10.1016/j.isci.2019.08.057.
Moutaoufik, M. T., Malty, R., Amin, S., Zhang, Q., Phanse, S., Gagarinova, A., Zilocchi, M., Hoell, L., Minic, Z., Gagarinova, M., Aoki, H., Stockwell, J., Jessulat, M., Goebels, F., Broderick, K., Scott, N. E., Vlasblom, J., Musso, G., Prasad, B., Lamantea, E., Garavaglia, B., Rajput, A., Murayama, K., Okazaki, Y., Foster, L. J., Bader, G. D., Cayabyab, F. S., & Babu, M. (2019). Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience, 191114-1132. https://doi.org/10.1016/j.isci.2019.08.057
Moutaoufik, Mohamed Taha, et al. "Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis." iScience vol. 19 (2019): 1114-1132. doi: https://doi.org/10.1016/j.isci.2019.08.057
Moutaoufik MT, Malty R, Amin S, Zhang Q, Phanse S, Gagarinova A, Zilocchi M, Hoell L, Minic Z, Gagarinova M, Aoki H, Stockwell J, Jessulat M, Goebels F, Broderick K, Scott NE, Vlasblom J, Musso G, Prasad B, Lamantea E, Garavaglia B, Rajput A, Murayama K, Okazaki Y, Foster LJ, Bader GD, Cayabyab FS, Babu M. Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience. 2019 Sep 27;19:1114-1132. doi: 10.1016/j.isci.2019.08.057. Epub 2019 Sep 04. PMID: 31536960; PMCID: PMC6831851.
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A convergent molecular network underlying autism and congenital heart disease.

Cell systems

Rosenthal SB, Willsey HR, Xu Y, Mei Y, Dea J, Wang S, Curtis C, Sempou E, Khokha MK, Chi NC, Willsey AJ, Fisch KM, Ideker T.
PMID: 34411509
Cell Syst. 2021 Nov 17;12(11):1094-1107.e6. doi: 10.1016/j.cels.2021.07.009. Epub 2021 Aug 18.

Patients with neurodevelopmental disorders, including autism, have an elevated incidence of congenital heart disease, but the extent to which these conditions share molecular mechanisms remains unknown. Here, we use network genetics to identify a convergent molecular network underlying autism...

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Rosenthal SB, Willsey HR, Xu Y, et al. A convergent molecular network underlying autism and congenital heart disease. Cell Syst. 2021;12(11):1094-1107.e6doi: 10.1016/j.cels.2021.07.009.
Rosenthal, S. B., Willsey, H. R., Xu, Y., Mei, Y., Dea, J., Wang, S., Curtis, C., Sempou, E., Khokha, M. K., Chi, N. C., Willsey, A. J., Fisch, K. M., & Ideker, T. (2021). A convergent molecular network underlying autism and congenital heart disease. Cell systems, 12(11), 1094-1107.e6. https://doi.org/10.1016/j.cels.2021.07.009
Rosenthal, Sara Brin, et al. "A convergent molecular network underlying autism and congenital heart disease." Cell systems vol. 12,11 (2021): 1094-1107.e6. doi: https://doi.org/10.1016/j.cels.2021.07.009
Rosenthal SB, Willsey HR, Xu Y, Mei Y, Dea J, Wang S, Curtis C, Sempou E, Khokha MK, Chi NC, Willsey AJ, Fisch KM, Ideker T. A convergent molecular network underlying autism and congenital heart disease. Cell Syst. 2021 Nov 17;12(11):1094-1107.e6. doi: 10.1016/j.cels.2021.07.009. Epub 2021 Aug 18. PMID: 34411509; PMCID: PMC8602730.
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Showing 1 to 12 of 21 entries