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Showing 1 to 9 of 9 entries
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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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BRCA1 and RNAi factors promote repair mediated by small RNAs and PALB2-RAD52.

Nature

Hatchi E, Goehring L, Landini S, Skourti-Stathaki K, DeConti DK, Abderazzaq FO, Banerjee P, Demers TM, Wang YE, Quackenbush J, Livingston DM.
PMID: 33536619
Nature. 2021 Mar;591(7851):665-670. doi: 10.1038/s41586-020-03150-2. Epub 2021 Feb 03.

Strong connections exist between R-loops (three-stranded structures harbouring an RNA:DNA hybrid and a displaced single-strand DNA), genome instability and human disease

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Hatchi E, Goehring L, Landini S, et al. BRCA1 and RNAi factors promote repair mediated by small RNAs and PALB2-RAD52. Nature. 2021;591(7851):665-670doi: 10.1038/s41586-020-03150-2.
Hatchi, E., Goehring, L., Landini, S., Skourti-Stathaki, K., DeConti, D. K., Abderazzaq, F. O., Banerjee, P., Demers, T. M., Wang, Y. E., Quackenbush, J., & Livingston, D. M. (2021). BRCA1 and RNAi factors promote repair mediated by small RNAs and PALB2-RAD52. Nature, 591(7851), 665-670. https://doi.org/10.1038/s41586-020-03150-2
Hatchi, Elodie, et al. "BRCA1 and RNAi factors promote repair mediated by small RNAs and PALB2-RAD52." Nature vol. 591,7851 (2021): 665-670. doi: https://doi.org/10.1038/s41586-020-03150-2
Hatchi E, Goehring L, Landini S, Skourti-Stathaki K, DeConti DK, Abderazzaq FO, Banerjee P, Demers TM, Wang YE, Quackenbush J, Livingston DM. BRCA1 and RNAi factors promote repair mediated by small RNAs and PALB2-RAD52. Nature. 2021 Mar;591(7851):665-670. doi: 10.1038/s41586-020-03150-2. Epub 2021 Feb 03. PMID: 33536619; PMCID: PMC8245199.
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Deep learning classification of lung cancer histology using CT images.

Scientific reports

Chaunzwa TL, Hosny A, Xu Y, Shafer A, Diao N, Lanuti M, Christiani DC, Mak RH, Aerts HJWL.
PMID: 33727623
Sci Rep. 2021 Mar 09;11(1):5471. doi: 10.1038/s41598-021-84630-x.

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to...

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Chaunzwa TL, Hosny A, Xu Y, et al. Deep learning classification of lung cancer histology using CT images. Sci Rep. 2021;11(1):5471doi: 10.1038/s41598-021-84630-x.
Chaunzwa, T. L., Hosny, A., Xu, Y., Shafer, A., Diao, N., Lanuti, M., Christiani, D. C., Mak, R. H., & Aerts, H. J. W. L. (2021). Deep learning classification of lung cancer histology using CT images. Scientific reports, 11(1), 5471. https://doi.org/10.1038/s41598-021-84630-x
Chaunzwa, Tafadzwa L, et al. "Deep learning classification of lung cancer histology using CT images." Scientific reports vol. 11,1 (2021): 5471. doi: https://doi.org/10.1038/s41598-021-84630-x
Chaunzwa TL, Hosny A, Xu Y, Shafer A, Diao N, Lanuti M, Christiani DC, Mak RH, Aerts HJWL. Deep learning classification of lung cancer histology using CT images. Sci Rep. 2021 Mar 09;11(1):5471. doi: 10.1038/s41598-021-84630-x. PMID: 33727623; PMCID: PMC7943565.
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Artificial intelligence for clinical oncology.

Cancer cell

Kann BH, Hosny A, Aerts HJWL.
PMID: 33930310
Cancer Cell. 2021 Jul 12;39(7):916-927. doi: 10.1016/j.ccell.2021.04.002. Epub 2021 Apr 29.

Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer care. With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of...

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Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell. 2021;39(7):916-927doi: 10.1016/j.ccell.2021.04.002.
Kann, B. H., Hosny, A., & Aerts, H. J. W. L. (2021). Artificial intelligence for clinical oncology. Cancer cell, 39(7), 916-927. https://doi.org/10.1016/j.ccell.2021.04.002
Kann, Benjamin H, et al. "Artificial intelligence for clinical oncology." Cancer cell vol. 39,7 (2021): 916-927. doi: https://doi.org/10.1016/j.ccell.2021.04.002
Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell. 2021 Jul 12;39(7):916-927. doi: 10.1016/j.ccell.2021.04.002. Epub 2021 Apr 29. PMID: 33930310; PMCID: PMC8282694.
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Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer.

NPJ digital medicine

Zeleznik R, Weiss J, Taron J, Guthier C, Bitterman DS, Hancox C, Kann BH, Kim DW, Punglia RS, Bredfeldt J, Foldyna B, Eslami P, Lu MT, Hoffmann U, Mak R, Aerts HJWL.
PMID: 33674717
NPJ Digit Med. 2021 Mar 05;4(1):43. doi: 10.1038/s41746-021-00416-5.

Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT)...

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Zeleznik R, Weiss J, Taron J, et al. Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer. NPJ Digit Med. 2021;4(1):43doi: 10.1038/s41746-021-00416-5.
Zeleznik, R., Weiss, J., Taron, J., Guthier, C., Bitterman, D. S., Hancox, C., Kann, B. H., Kim, D. W., Punglia, R. S., Bredfeldt, J., Foldyna, B., Eslami, P., Lu, M. T., Hoffmann, U., Mak, R., & Aerts, H. J. W. L. (2021). Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer. NPJ digital medicine, 4(1), 43. https://doi.org/10.1038/s41746-021-00416-5
Zeleznik, Roman, et al. "Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer." NPJ digital medicine vol. 4,1 (2021): 43. doi: https://doi.org/10.1038/s41746-021-00416-5
Zeleznik R, Weiss J, Taron J, Guthier C, Bitterman DS, Hancox C, Kann BH, Kim DW, Punglia RS, Bredfeldt J, Foldyna B, Eslami P, Lu MT, Hoffmann U, Mak R, Aerts HJWL. Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer. NPJ Digit Med. 2021 Mar 05;4(1):43. doi: 10.1038/s41746-021-00416-5. PMID: 33674717; PMCID: PMC7935874.
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Data Science in Radiology: A Path Forward.

Clinical cancer research : an official journal of the American Association for Cancer Research

Aerts HJWL.
PMID: 29097379
Clin Cancer Res. 2018 Feb 01;24(3):532-534. doi: 10.1158/1078-0432.CCR-17-2804. Epub 2017 Nov 02.

Artificial intelligence (AI), especially deep learning, has the potential to fundamentally alter clinical radiology. AI algorithms, which excel in quantifying complex patterns in data, have shown remarkable progress in applications ranging from self-driving cars to speech recognition. The AI...

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Aerts HJWL. Data Science in Radiology: A Path Forward. Clin Cancer Res. 2017;24(3):532-534doi: 10.1158/1078-0432.CCR-17-2804.
Aerts, H. J. W. L. (2018). Data Science in Radiology: A Path Forward. Clinical cancer research : an official journal of the American Association for Cancer Research, 24(3), 532-534. https://doi.org/10.1158/1078-0432.CCR-17-2804
Aerts, Hugo J W L. "Data Science in Radiology: A Path Forward." Clinical cancer research : an official journal of the American Association for Cancer Research vol. 24,3 (2018): 532-534. doi: https://doi.org/10.1158/1078-0432.CCR-17-2804
Aerts HJWL. Data Science in Radiology: A Path Forward. Clin Cancer Res. 2018 Feb 01;24(3):532-534. doi: 10.1158/1078-0432.CCR-17-2804. Epub 2017 Nov 02. PMID: 29097379; PMCID: PMC5810958.
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Histopathological Image QTL Discovery of Immune Infiltration Variants.

iScience

Barry JD, Fagny M, Paulson JN, Aerts HJWL, Platig J, Quackenbush J.
PMID: 30240647
iScience. 2018 Jul 27;5:80-89. doi: 10.1016/j.isci.2018.07.001. Epub 2018 Jul 06.

Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype...

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Barry JD, Fagny M, Paulson JN, et al. Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience. 2018;5:80-89doi: 10.1016/j.isci.2018.07.001.
Barry, J. D., Fagny, M., Paulson, J. N., Aerts, H. J. W. L., Platig, J., & Quackenbush, J. (2018). Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience, 580-89. https://doi.org/10.1016/j.isci.2018.07.001
Barry, Joseph D, et al. "Histopathological Image QTL Discovery of Immune Infiltration Variants." iScience vol. 5 (2018): 80-89. doi: https://doi.org/10.1016/j.isci.2018.07.001
Barry JD, Fagny M, Paulson JN, Aerts HJWL, Platig J, Quackenbush J. Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience. 2018 Jul 27;5:80-89. doi: 10.1016/j.isci.2018.07.001. Epub 2018 Jul 06. PMID: 30240647; PMCID: PMC6123851.
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Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.

Frontiers in oncology

Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, Mak R, Aerts HJ.
PMID: 27064691
Front Oncol. 2016 Mar 30;6:71. doi: 10.3389/fonc.2016.00071. eCollection 2016.

BACKGROUND: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell...

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Wu W, Parmar C, Grossmann P, et al. Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology. Front Oncol. 2016;6:71doi: 10.3389/fonc.2016.00071.
Wu, W., Parmar, C., Grossmann, P., Quackenbush, J., Lambin, P., Bussink, J., Mak, R., & Aerts, H. J. (2016). Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology. Frontiers in oncology, 671. https://doi.org/10.3389/fonc.2016.00071
Wu, Weimiao, et al. "Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology." Frontiers in oncology vol. 6 (2016): 71. doi: https://doi.org/10.3389/fonc.2016.00071
Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, Mak R, Aerts HJ. Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology. Front Oncol. 2016 Mar 30;6:71. doi: 10.3389/fonc.2016.00071. eCollection 2016. PMID: 27064691; PMCID: PMC4811956.
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Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer.

Frontiers in oncology

Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ.
PMID: 26697407
Front Oncol. 2015 Dec 03;5:272. doi: 10.3389/fonc.2015.00272. eCollection 2015.

INTRODUCTION: "Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance...

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Parmar C, Grossmann P, Rietveld D, et al. Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer. Front Oncol. 2015;5:272doi: 10.3389/fonc.2015.00272.
Parmar, C., Grossmann, P., Rietveld, D., Rietbergen, M. M., Lambin, P., & Aerts, H. J. (2015). Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer. Frontiers in oncology, 5272. https://doi.org/10.3389/fonc.2015.00272
Parmar, Chintan, et al. "Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer." Frontiers in oncology vol. 5 (2015): 272. doi: https://doi.org/10.3389/fonc.2015.00272
Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ. Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer. Front Oncol. 2015 Dec 03;5:272. doi: 10.3389/fonc.2015.00272. eCollection 2015. PMID: 26697407; PMCID: PMC4668290.
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Showing 1 to 9 of 9 entries