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Localization of epileptic seizure focus by computerized analysis of fMRI recordings.

Brain informatics

Hekmati R, Azencott R, Zhang W, Chu ZD, Paldino MJ.
PMID: 33128629
Brain Inform. 2020 Oct 31;7(1):13. doi: 10.1186/s40708-020-00114-0.

By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of...

Cite
Hekmati R, Azencott R, Zhang W, et al. Localization of epileptic seizure focus by computerized analysis of fMRI recordings. Brain Inform. 2020;7(1):13doi: 10.1186/s40708-020-00114-0.
Hekmati, R., Azencott, R., Zhang, W., Chu, Z. D., & Paldino, M. J. (2020). Localization of epileptic seizure focus by computerized analysis of fMRI recordings. Brain informatics, 7(1), 13. https://doi.org/10.1186/s40708-020-00114-0
Hekmati, Rasoul, et al. "Localization of epileptic seizure focus by computerized analysis of fMRI recordings." Brain informatics vol. 7,1 (2020): 13. doi: https://doi.org/10.1186/s40708-020-00114-0
Hekmati R, Azencott R, Zhang W, Chu ZD, Paldino MJ. Localization of epileptic seizure focus by computerized analysis of fMRI recordings. Brain Inform. 2020 Oct 31;7(1):13. doi: 10.1186/s40708-020-00114-0. PMID: 33128629; PMCID: PMC7603444.
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Nonlinear post-selection inference for genome-wide association studies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Slim L, Chatelain C, Azencott CA.
PMID: 34890162
Pac Symp Biocomput. 2022;27:349-360.

To address the lack of statistical power and interpretability of genome-wide association studies (GWAS), gene-level analyses combine the p-values of individual single nucleotide polymorphisms (SNPs) into gene statistics. However, using all SNPs mapped to a gene, including those with...

Cite
Slim L, Chatelain C, Azencott CA. Nonlinear post-selection inference for genome-wide association studies. Pac Symp Biocomput. 2022;27:349-360
Slim, L., Chatelain, C., & Azencott, C. A. (2022). Nonlinear post-selection inference for genome-wide association studies. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 27349-360.
Slim, Lotfi, et al. "Nonlinear post-selection inference for genome-wide association studies." Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing vol. 27 (2022): 349-360.
Slim L, Chatelain C, Azencott CA. Nonlinear post-selection inference for genome-wide association studies. Pac Symp Biocomput. 2022;27:349-360. PMID: 34890162.
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Genome-wide perturbations by miRNAs map onto functional cellular pathways, identifying regulators of chromatin modifiers.

NPJ systems biology and applications

Moss TJ, Luo Z, Seviour EG, Sehgal V, Lu Y, Hill SM, Rupaimoole R, Lee JS, Rodriguez-Aguayo C, Lopez-Berestein G, Sood AK, Azencott R, Gray JW, Mukherjee S, Mills GB, Ram PT.
PMID: 28725457
NPJ Syst Biol Appl. 2015 Sep 28;1:15001. doi: 10.1038/npjsba.2015.1. eCollection 2015.

BACKGROUND: Regulation of gene expression by microRNAs (miRNAs) is critical for determining cellular fate and function. Dysregulation of miRNA expression contributes to the development and progression of multiple diseases. miRNA can target multiple mRNAs, making deconvolution of the effects...

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Moss TJ, Luo Z, Seviour EG, et al. Genome-wide perturbations by miRNAs map onto functional cellular pathways, identifying regulators of chromatin modifiers. NPJ Syst Biol Appl. 2015;1:15001doi: 10.1038/npjsba.2015.1.
Moss, T. J., Luo, Z., Seviour, E. G., Sehgal, V., Lu, Y., Hill, S. M., Rupaimoole, R., Lee, J. S., Rodriguez-Aguayo, C., Lopez-Berestein, G., Sood, A. K., Azencott, R., Gray, J. W., Mukherjee, S., Mills, G. B., & Ram, P. T. (2015). Genome-wide perturbations by miRNAs map onto functional cellular pathways, identifying regulators of chromatin modifiers. NPJ systems biology and applications, 115001. https://doi.org/10.1038/npjsba.2015.1
Moss, Tyler J, et al. "Genome-wide perturbations by miRNAs map onto functional cellular pathways, identifying regulators of chromatin modifiers." NPJ systems biology and applications vol. 1 (2015): 15001. doi: https://doi.org/10.1038/npjsba.2015.1
Moss TJ, Luo Z, Seviour EG, Sehgal V, Lu Y, Hill SM, Rupaimoole R, Lee JS, Rodriguez-Aguayo C, Lopez-Berestein G, Sood AK, Azencott R, Gray JW, Mukherjee S, Mills GB, Ram PT. Genome-wide perturbations by miRNAs map onto functional cellular pathways, identifying regulators of chromatin modifiers. NPJ Syst Biol Appl. 2015 Sep 28;1:15001. doi: 10.1038/npjsba.2015.1. eCollection 2015. PMID: 28725457; PMCID: PMC5516802.
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Multitask group Lasso for Genome Wide association Studies in diverse populations.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Nouira A, Azencott CA.
PMID: 34890146
Pac Symp Biocomput. 2022;27:163-174.

Genome-Wide Association Studies, or GWAS, aim at finding Single Nucleotide Polymorphisms (SNPs) that are associated with a phenotype of interest. GWAS are known to suffer from the large dimensionality of the data with respect to the number of available...

Cite
Nouira A, Azencott CA. Multitask group Lasso for Genome Wide association Studies in diverse populations. Pac Symp Biocomput. 2022;27:163-174
Nouira, A., & Azencott, C. A. (2022). Multitask group Lasso for Genome Wide association Studies in diverse populations. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 27163-174.
Nouira, Asma, and Azencott, Chloé-Agathe. "Multitask group Lasso for Genome Wide association Studies in diverse populations." Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing vol. 27 (2022): 163-174.
Nouira A, Azencott CA. Multitask group Lasso for Genome Wide association Studies in diverse populations. Pac Symp Biocomput. 2022;27:163-174. PMID: 34890146.
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Functional Thin Films on Surfaces.

IEEE transactions on visualization and computer graphics

Vantzos O, Azencot O, Wardeztky M, Rumpf M, Ben-Chen M.
PMID: 28113979
IEEE Trans Vis Comput Graph. 2017 Mar;23(3):1179-1192. doi: 10.1109/TVCG.2016.2605083. Epub 2016 Sep 01.

The motion of a thin viscous film of fluid on a curved surface exhibits many intricate visual phenomena, which are challenging to simulate using existing techniques. A possible alternative is to use a reduced model, involving only the temporal...

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Vantzos O, Azencot O, Wardeztky M, et al. Functional Thin Films on Surfaces. IEEE Trans Vis Comput Graph. 2016;23(3):1179-1192doi: 10.1109/TVCG.2016.2605083.
Vantzos, O., Azencot, O., Wardeztky, M., Rumpf, M., & Ben-Chen, M. (2017). Functional Thin Films on Surfaces. IEEE transactions on visualization and computer graphics, 23(3), 1179-1192. https://doi.org/10.1109/TVCG.2016.2605083
Vantzos, Orestis, et al. "Functional Thin Films on Surfaces." IEEE transactions on visualization and computer graphics vol. 23,3 (2017): 1179-1192. doi: https://doi.org/10.1109/TVCG.2016.2605083
Vantzos O, Azencot O, Wardeztky M, Rumpf M, Ben-Chen M. Functional Thin Films on Surfaces. IEEE Trans Vis Comput Graph. 2017 Mar;23(3):1179-1192. doi: 10.1109/TVCG.2016.2605083. Epub 2016 Sep 01. PMID: 28113979.
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Binary Markov Random Fields and interpretable mass spectra discrimination.

Statistical applications in genetics and molecular biology

Kong A, Azencott R.
PMID: 28475101
Stat Appl Genet Mol Biol. 2017 Feb 11; doi: 10.1515/sagmb-2016-0019. Epub 2017 Feb 11.

For mass spectra acquired from cancer patients by MALDI or SELDI techniques, automated discrimination between cancer types or stages has often been implemented by machine learning algorithms. Nevertheless, these techniques typically lack interpretability in terms of biomarkers. In this...

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Kong A, Azencott R. Binary Markov Random Fields and interpretable mass spectra discrimination. Stat Appl Genet Mol Biol. 2017;doi: 10.1515/sagmb-2016-0019.
Kong, A., & Azencott, R. (2017). Binary Markov Random Fields and interpretable mass spectra discrimination. Statistical applications in genetics and molecular biology, . https://doi.org/10.1515/sagmb-2016-0019
Kong, Ao, and Azencott, Robert. "Binary Markov Random Fields and interpretable mass spectra discrimination." Statistical applications in genetics and molecular biology vol. (2017). doi: https://doi.org/10.1515/sagmb-2016-0019
Kong A, Azencott R. Binary Markov Random Fields and interpretable mass spectra discrimination. Stat Appl Genet Mol Biol. 2017 Feb 11; doi: 10.1515/sagmb-2016-0019. Epub 2017 Feb 11. PMID: 28475101.
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Erratum: Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

Nature communications

Sieberts SK, Zhu F, García-García J, Stahl E, Pratap A, Pandey G, Pappas D, Aguilar D, Anton B, Bonet J, Eksi R, Fornés O, Guney E, Li H, Marín MA, Panwar B, Planas-Iglesias J, Poglayen D, Cui J, Falcao AO, Suver C, Hoff B, Balagurusamy VSK, Dillenberger D, Neto EC, Norman T, Aittokallio T, Ammad-Ud-Din M, Azencott CA, Bellón V, Boeva V, Bunte K, Chheda H, Cheng L, Corander J, Dumontier M, Goldenberg A, Gopalacharyulu P, Hajiloo M, Hidru D, Jaiswal A, Kaski S, Khalfaoui B, Khan SA, Kramer ER, Marttinen P, Mezlini AM, Molparia B, Pirinen M, Saarela J, Samwald M, Stoven V, Tang H, Tang J, Torkamani A, Vert JP, Wang B, Wang T, Wennerberg K, Wineinger NE, Xiao G, Xie Y, Yeung R, Zhan X, Zhao C, Greenberg J, Kremer J, Michaud K, Barton A, Coenen M, Mariette X, Miceli C, Shadick N, Weinblatt M, de Vries N, Tak PP, Gerlag D, Huizinga TWJ, Kurreeman F, Allaart CF, Bridges SL, Criswell L, Moreland L, Klareskog L, Saevarsdottir S, Padyukov L, Gregersen PK, Friend S, Plenge R, Stolovitzky G, Oliva B, Guan Y, Mangravite LM.
PMID: 27721464
Nat Commun. 2016 Oct 10;7:13205. doi: 10.1038/ncomms13205.

No abstract available.

Cite
Sieberts SK, Zhu F, García-García J, et al. Erratum: Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nat Commun. 2016;7:13205doi: 10.1038/ncomms13205.
Sieberts, S. K., Zhu, F., García-García, J., Stahl, E., Pratap, A., Pandey, G., Pappas, D., Aguilar, D., Anton, B., Bonet, J., Eksi, R., Fornés, O., Guney, E., Li, H., Marín, M. A., Panwar, B., Planas-Iglesias, J., Poglayen, D., Cui, J., Falcao, A. O., Suver, C., Hoff, B., Balagurusamy, V. S. K., Dillenberger, D., Neto, E. C., Norman, T., Aittokallio, T., Ammad-Ud-Din, M., Azencott, C. A., Bellón, V., Boeva, V., Bunte, K., Chheda, H., Cheng, L., Corander, J., Dumontier, M., Goldenberg, A., Gopalacharyulu, P., Hajiloo, M., Hidru, D., Jaiswal, A., Kaski, S., Khalfaoui, B., Khan, S. A., Kramer, E. R., Marttinen, P., Mezlini, A. M., Molparia, B., Pirinen, M., Saarela, J., Samwald, M., Stoven, V., Tang, H., Tang, J., Torkamani, A., Vert, J. P., Wang, B., Wang, T., Wennerberg, K., Wineinger, N. E., Xiao, G., Xie, Y., Yeung, R., Zhan, X., Zhao, C., Greenberg, J., Kremer, J., Michaud, K., Barton, A., Coenen, M., Mariette, X., Miceli, C., Shadick, N., Weinblatt, M., de Vries, N., Tak, P. P., Gerlag, D., Huizinga, T. W. J., Kurreeman, F., Allaart, C. F., Bridges, S. L., Criswell, L., Moreland, L., Klareskog, L., Saevarsdottir, S., Padyukov, L., Gregersen, P. K., Friend, S., Plenge, R., Stolovitzky, G., Oliva, B., Guan, Y., & Mangravite, L. M. (2016). Erratum: Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature communications, 713205. https://doi.org/10.1038/ncomms13205
Sieberts, Solveig K, et al. "Erratum: Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis." Nature communications vol. 7 (2016): 13205. doi: https://doi.org/10.1038/ncomms13205
Sieberts SK, Zhu F, García-García J, Stahl E, Pratap A, Pandey G, Pappas D, Aguilar D, Anton B, Bonet J, Eksi R, Fornés O, Guney E, Li H, Marín MA, Panwar B, Planas-Iglesias J, Poglayen D, Cui J, Falcao AO, Suver C, Hoff B, Balagurusamy VSK, Dillenberger D, Neto EC, Norman T, Aittokallio T, Ammad-Ud-Din M, Azencott CA, Bellón V, Boeva V, Bunte K, Chheda H, Cheng L, Corander J, Dumontier M, Goldenberg A, Gopalacharyulu P, Hajiloo M, Hidru D, Jaiswal A, Kaski S, Khalfaoui B, Khan SA, Kramer ER, Marttinen P, Mezlini AM, Molparia B, Pirinen M, Saarela J, Samwald M, Stoven V, Tang H, Tang J, Torkamani A, Vert JP, Wang B, Wang T, Wennerberg K, Wineinger NE, Xiao G, Xie Y, Yeung R, Zhan X, Zhao C, Greenberg J, Kremer J, Michaud K, Barton A, Coenen M, Mariette X, Miceli C, Shadick N, Weinblatt M, de Vries N, Tak PP, Gerlag D, Huizinga TWJ, Kurreeman F, Allaart CF, Bridges SL, Criswell L, Moreland L, Klareskog L, Saevarsdottir S, Padyukov L, Gregersen PK, Friend S, Plenge R, Stolovitzky G, Oliva B, Guan Y, Mangravite LM. Erratum: Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nat Commun. 2016 Oct 10;7:13205. doi: 10.1038/ncomms13205. PMID: 27721464; PMCID: PMC5062501.
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Functional proteomics identifies miRNAs to target a p27/Myc/phospho-Rb signature in breast and ovarian cancer.

Oncogene

Seviour EG, Sehgal V, Lu Y, Luo Z, Moss T, Zhang F, Hill SM, Liu W, Maiti SN, Cooper L, Azencot R, Lopez-Berestein G, Rodriguez-Aguayo C, Roopaimoole R, Pecot CV, Sood AK, Mukherjee S, Gray JW, Mills GB, Ram PT.
PMID: 26865225
Oncogene. 2016 Feb 11;35(6):801. doi: 10.1038/onc.2015.177.

No abstract available.

Cite
Seviour EG, Sehgal V, Lu Y, et al. Functional proteomics identifies miRNAs to target a p27/Myc/phospho-Rb signature in breast and ovarian cancer. Oncogene. 2016;35(6):801doi: 10.1038/onc.2015.177.
Seviour, E. G., Sehgal, V., Lu, Y., Luo, Z., Moss, T., Zhang, F., Hill, S. M., Liu, W., Maiti, S. N., Cooper, L., Azencot, R., Lopez-Berestein, G., Rodriguez-Aguayo, C., Roopaimoole, R., Pecot, C. V., Sood, A. K., Mukherjee, S., Gray, J. W., Mills, G. B., & Ram, P. T. (2016). Functional proteomics identifies miRNAs to target a p27/Myc/phospho-Rb signature in breast and ovarian cancer. Oncogene, 35(6), 801. https://doi.org/10.1038/onc.2015.177
Seviour, E G, et al. "Functional proteomics identifies miRNAs to target a p27/Myc/phospho-Rb signature in breast and ovarian cancer." Oncogene vol. 35,6 (2016): 801. doi: https://doi.org/10.1038/onc.2015.177
Seviour EG, Sehgal V, Lu Y, Luo Z, Moss T, Zhang F, Hill SM, Liu W, Maiti SN, Cooper L, Azencot R, Lopez-Berestein G, Rodriguez-Aguayo C, Roopaimoole R, Pecot CV, Sood AK, Mukherjee S, Gray JW, Mills GB, Ram PT. Functional proteomics identifies miRNAs to target a p27/Myc/phospho-Rb signature in breast and ovarian cancer. Oncogene. 2016 Feb 11;35(6):801. doi: 10.1038/onc.2015.177. PMID: 26865225; PMCID: PMC7608397.
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Multitask group Lasso for Genome Wide association Studies in diverse populations.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Nouira A, Azencott CA.
PMID: 34890146
Pac Symp Biocomput. 2022;27:163-174.

Genome-Wide Association Studies, or GWAS, aim at finding Single Nucleotide Polymorphisms (SNPs) that are associated with a phenotype of interest. GWAS are known to suffer from the large dimensionality of the data with respect to the number of available...

Cite
Nouira A, Azencott CA. Multitask group Lasso for Genome Wide association Studies in diverse populations. Pac Symp Biocomput. 2022;27:163-174
Nouira, A., & Azencott, C. A. (2022). Multitask group Lasso for Genome Wide association Studies in diverse populations. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 27163-174.
Nouira, Asma, and Azencott, Chloé-Agathe. "Multitask group Lasso for Genome Wide association Studies in diverse populations." Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing vol. 27 (2022): 163-174.
Nouira A, Azencott CA. Multitask group Lasso for Genome Wide association Studies in diverse populations. Pac Symp Biocomput. 2022;27:163-174. PMID: 34890146.
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Where Do We Stand in Regularization for Life Science Studies?.

Journal of computational biology : a journal of computational molecular cell biology

Tozzo V, Azencott CA, Fiorini S, Fava E, Trucco A, Barla A.
PMID: 33926217
J Comput Biol. 2021 Apr 29; doi: 10.1089/cmb.2019.0371. Epub 2021 Apr 29.

More and more biologists and bioinformaticians turn to machine learning to analyze large amounts of data. In this context, it is crucial to understand which is the most suitable data analysis pipeline for achieving reliable results. This process may...

Cite
Tozzo V, Azencott CA, Fiorini S, et al. Where Do We Stand in Regularization for Life Science Studies?. J Comput Biol. 2021;doi: 10.1089/cmb.2019.0371.
Tozzo, V., Azencott, C. A., Fiorini, S., Fava, E., Trucco, A., & Barla, A. (2021). Where Do We Stand in Regularization for Life Science Studies?. Journal of computational biology : a journal of computational molecular cell biology, . https://doi.org/10.1089/cmb.2019.0371
Tozzo, Veronica, et al. "Where Do We Stand in Regularization for Life Science Studies?." Journal of computational biology : a journal of computational molecular cell biology vol. (2021). doi: https://doi.org/10.1089/cmb.2019.0371
Tozzo V, Azencott CA, Fiorini S, Fava E, Trucco A, Barla A. Where Do We Stand in Regularization for Life Science Studies?. J Comput Biol. 2021 Apr 29; doi: 10.1089/cmb.2019.0371. Epub 2021 Apr 29. PMID: 33926217.
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Nonlinear post-selection inference for genome-wide association studies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Slim L, Chatelain C, Azencott CA.
PMID: 34890162
Pac Symp Biocomput. 2022;27:349-360.

To address the lack of statistical power and interpretability of genome-wide association studies (GWAS), gene-level analyses combine the p-values of individual single nucleotide polymorphisms (SNPs) into gene statistics. However, using all SNPs mapped to a gene, including those with...

Cite
Slim L, Chatelain C, Azencott CA. Nonlinear post-selection inference for genome-wide association studies. Pac Symp Biocomput. 2022;27:349-360
Slim, L., Chatelain, C., & Azencott, C. A. (2022). Nonlinear post-selection inference for genome-wide association studies. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 27349-360.
Slim, Lotfi, et al. "Nonlinear post-selection inference for genome-wide association studies." Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing vol. 27 (2022): 349-360.
Slim L, Chatelain C, Azencott CA. Nonlinear post-selection inference for genome-wide association studies. Pac Symp Biocomput. 2022;27:349-360. PMID: 34890162.
Copy Download .nbib Format: NLM
Nonlinear post-selection inference for genome-wide association studies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Slim L, Chatelain C, Azencott CA.
PMID: 34890162
Pac Symp Biocomput. 2022;27:349-360.

To address the lack of statistical power and interpretability of genome-wide association studies (GWAS), gene-level analyses combine the p-values of individual single nucleotide polymorphisms (SNPs) into gene statistics. However, using all SNPs mapped to a gene, including those with...

Cite
Slim L, Chatelain C, Azencott CA. Nonlinear post-selection inference for genome-wide association studies. Pac Symp Biocomput. 2022;27:349-360
Slim, L., Chatelain, C., & Azencott, C. A. (2022). Nonlinear post-selection inference for genome-wide association studies. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 27349-360.
Slim, Lotfi, et al. "Nonlinear post-selection inference for genome-wide association studies." Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing vol. 27 (2022): 349-360.
Slim L, Chatelain C, Azencott CA. Nonlinear post-selection inference for genome-wide association studies. Pac Symp Biocomput. 2022;27:349-360. PMID: 34890162.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 13 entries