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Showing 1 to 12 of 14 entries
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Towards an Oncology Database (ONCOD) Using a Warehousing Approach.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

Wang X, Liu L, Fackenthal J, Chang P, Newstead G, Chmura S, Foster I, Olopade OI.
PMID: 22779060
AMIA Jt Summits Transl Sci Proc. 2012;2012:105. Epub 2012 Mar 19.

Here we report a model data mart developed upon a warehousing system focusing on oncology data to explore optimized system architecture to support enhanced data integration and application capacity.

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Wang X, Liu L, Fackenthal J, et al. Towards an Oncology Database (ONCOD) Using a Warehousing Approach. AMIA Jt Summits Transl Sci Proc. 2012;2012:105
Wang, X., Liu, L., Fackenthal, J., Chang, P., Newstead, G., Chmura, S., Foster, I., & Olopade, O. I. (2012). Towards an Oncology Database (ONCOD) Using a Warehousing Approach. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2012105.
Wang, Xiaoming, et al. "Towards an Oncology Database (ONCOD) Using a Warehousing Approach." AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science vol. 2012 (2012): 105.
Wang X, Liu L, Fackenthal J, Chang P, Newstead G, Chmura S, Foster I, Olopade OI. Towards an Oncology Database (ONCOD) Using a Warehousing Approach. AMIA Jt Summits Transl Sci Proc. 2012;2012:105. Epub 2012 Mar 19. PMID: 22779060; PMCID: PMC3392051.
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Integrating Epigenomics into Pharmacogenomic Studies.

Pharmacogenomics and personalized medicine

Zhang W, Huang RS, Dolan ME.
PMID: 20622972
Pharmgenomics Pers Med. 2008 Nov;2008(1):7-14. doi: 10.2147/pgpm.s4341.

The goal of personalized medicine is to recommend drug treatment based on an individual's genetic makeup. Pharmacogenomic studies utilize two main approaches: candidate gene and whole-genome. Both approaches analyze genetic variants such as single nucleotide polymorphisms (SNPs) to identify...

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Zhang W, Huang RS, Dolan ME. Integrating Epigenomics into Pharmacogenomic Studies. Pharmgenomics Pers Med. 2008;2008(1):7-14doi: 10.2147/pgpm.s4341.
Zhang, W., Huang, R. S., & Dolan, M. E. (2008). Integrating Epigenomics into Pharmacogenomic Studies. Pharmacogenomics and personalized medicine, 2008(1), 7-14. https://doi.org/10.2147/pgpm.s4341
Zhang, Wei, et al. "Integrating Epigenomics into Pharmacogenomic Studies." Pharmacogenomics and personalized medicine vol. 2008,1 (2008): 7-14. doi: https://doi.org/10.2147/pgpm.s4341
Zhang W, Huang RS, Dolan ME. Integrating Epigenomics into Pharmacogenomic Studies. Pharmgenomics Pers Med. 2008 Nov;2008(1):7-14. doi: 10.2147/pgpm.s4341. PMID: 20622972; PMCID: PMC2901114.
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MYC and Breast Cancer.

Genes & cancer

Xu J, Chen Y, Olopade OI.
PMID: 21779462
Genes Cancer. 2010 Jun;1(6):629-40. doi: 10.1177/1947601910378691.

MYC is a key regulator of cell growth, proliferation, metabolism, differentiation, and apoptosis. MYC deregulation contributes to breast cancer development and progression and is associated with poor outcomes. Multiple mechanisms are involved in MYC deregulation in breast cancer, including...

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Xu J, Chen Y, Olopade OI. MYC and Breast Cancer. Genes Cancer. 2010;1(6):629-40doi: 10.1177/1947601910378691.
Xu, J., Chen, Y., & Olopade, O. I. (2010). MYC and Breast Cancer. Genes & cancer, 1(6), 629-40. https://doi.org/10.1177/1947601910378691
Xu, Jinhua, et al. "MYC and Breast Cancer." Genes & cancer vol. 1,6 (2010): 629-40. doi: https://doi.org/10.1177/1947601910378691
Xu J, Chen Y, Olopade OI. MYC and Breast Cancer. Genes Cancer. 2010 Jun;1(6):629-40. doi: 10.1177/1947601910378691. PMID: 21779462; PMCID: PMC3092228.
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Investigation of Sparse Data Mouse Imaging Using Micro-CT with a Carbon-Nanotube-Based X-ray Source.

Tsinghua science and technology

Bian J, Han X, Sidky EY, Cao G, Lu J, Zhou O, Pan X.
PMID: 20582237
Tsinghua Sci Technol. 2010 Feb 01;15(1):74-78. doi: 10.1016/s1007-0214(10)70012-2.

There has been a renewed interest in algorithm development for image reconstruction from highly incomplete data in computed tomography (CT). Such algorithms may lead to reduced imaging dose and time, and to the design of innovative configurations tailored to...

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Bian J, Han X, Sidky EY, et al. Investigation of Sparse Data Mouse Imaging Using Micro-CT with a Carbon-Nanotube-Based X-ray Source. Tsinghua Sci Technol. 2010;15(1):74-78doi: 10.1016/s1007-0214(10)70012-2.
Bian, J., Han, X., Sidky, E. Y., Cao, G., Lu, J., Zhou, O., & Pan, X. (2010). Investigation of Sparse Data Mouse Imaging Using Micro-CT with a Carbon-Nanotube-Based X-ray Source. Tsinghua science and technology, 15(1), 74-78. https://doi.org/10.1016/s1007-0214(10)70012-2
Bian, Junguo, et al. "Investigation of Sparse Data Mouse Imaging Using Micro-CT with a Carbon-Nanotube-Based X-ray Source." Tsinghua science and technology vol. 15,1 (2010): 74-78. doi: https://doi.org/10.1016/s1007-0214(10)70012-2
Bian J, Han X, Sidky EY, Cao G, Lu J, Zhou O, Pan X. Investigation of Sparse Data Mouse Imaging Using Micro-CT with a Carbon-Nanotube-Based X-ray Source. Tsinghua Sci Technol. 2010 Feb 01;15(1):74-78. doi: 10.1016/s1007-0214(10)70012-2. PMID: 20582237; PMCID: PMC2891549.
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The Structural Basis of Action of Vanadyl (VO.

Coordination chemistry reviews

Makinen MW, Salehitazangi M.
PMID: 25237207
Coord Chem Rev. 2014 Nov 01;279:1-22. doi: 10.1016/j.ccr.2014.07.003.

Much emphasis has been given to vanadium compounds as potential therapeutic reagents for the treatment of diabetes mellitus. Thus far, no vanadium compound has proven efficacious for long-term treatment of this disease in humans. Therefore, in review of the...

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Makinen MW, Salehitazangi M. The Structural Basis of Action of Vanadyl (VO. Coord Chem Rev. 2014;279:1-22doi: 10.1016/j.ccr.2014.07.003.
Makinen, M. W., & Salehitazangi, M. (2014). The Structural Basis of Action of Vanadyl (VO. Coordination chemistry reviews, 2791-22. https://doi.org/10.1016/j.ccr.2014.07.003
Makinen, Marvin W, and Salehitazangi, Marzieh. "The Structural Basis of Action of Vanadyl (VO." Coordination chemistry reviews vol. 279 (2014): 1-22. doi: https://doi.org/10.1016/j.ccr.2014.07.003
Makinen MW, Salehitazangi M. The Structural Basis of Action of Vanadyl (VO. Coord Chem Rev. 2014 Nov 01;279:1-22. doi: 10.1016/j.ccr.2014.07.003. PMID: 25237207; PMCID: PMC4163958.
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Consistency Conditions for Cone-Beam CT Data Acquired with a Straight-Line Source Trajectory.

Tsinghua science and technology

Levine MS, Sidky EY, Pan X.
PMID: 20559455
Tsinghua Sci Technol. 2010 Feb;15(1):56-61. doi: 10.1016/S1007-0214(10)70009-2.

A consistency condition is developed for computed tomography (CT) projection data acquired from a straight-line X-ray source trajectory. The condition states that integrals of normalized projection data along detector lines parallel to the X-ray path must be equal. The...

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Levine MS, Sidky EY, Pan X. Consistency Conditions for Cone-Beam CT Data Acquired with a Straight-Line Source Trajectory. Tsinghua Sci Technol. 2010;15(1):56-61doi: 10.1016/S1007-0214(10)70009-2.
Levine, M. S., Sidky, E. Y., & Pan, X. (2010). Consistency Conditions for Cone-Beam CT Data Acquired with a Straight-Line Source Trajectory. Tsinghua science and technology, 15(1), 56-61. https://doi.org/10.1016/S1007-0214(10)70009-2
Levine, Margo S, et al. "Consistency Conditions for Cone-Beam CT Data Acquired with a Straight-Line Source Trajectory." Tsinghua science and technology vol. 15,1 (2010): 56-61. doi: https://doi.org/10.1016/S1007-0214(10)70009-2
Levine MS, Sidky EY, Pan X. Consistency Conditions for Cone-Beam CT Data Acquired with a Straight-Line Source Trajectory. Tsinghua Sci Technol. 2010 Feb;15(1):56-61. doi: 10.1016/S1007-0214(10)70009-2. PMID: 20559455; PMCID: PMC2886312.
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An Investigation of Compressive-sensing Image Reconstruction from Flying-focal-spot CT Data.

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium

Xia D, Bian J, Han X, Sidky EY, Pan X.
PMID: 21318098
IEEE Nucl Sci Symp Conf Rec (1997). 2009 Jan 01;2009:3458-3462. doi: 10.1109/NSSMIC.2009.5401787.

Flying-focal-spot (FFS) technique has been used for improving the sampling condition in advanced clinical CT by collecting multiple cone-beam data sets with the focal-spot at different locations at each "projection view". It has been demonstrated that the increased sampling...

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Xia D, Bian J, Han X, et al. An Investigation of Compressive-sensing Image Reconstruction from Flying-focal-spot CT Data. IEEE Nucl Sci Symp Conf Rec (1997). 2009;2009:3458-3462doi: 10.1109/NSSMIC.2009.5401787.
Xia, D., Bian, J., Han, X., Sidky, E. Y., & Pan, X. (2009). An Investigation of Compressive-sensing Image Reconstruction from Flying-focal-spot CT Data. IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium, 20093458-3462. https://doi.org/10.1109/NSSMIC.2009.5401787
Xia, D, et al. "An Investigation of Compressive-sensing Image Reconstruction from Flying-focal-spot CT Data." IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium vol. 2009 (2009): 3458-3462. doi: https://doi.org/10.1109/NSSMIC.2009.5401787
Xia D, Bian J, Han X, Sidky EY, Pan X. An Investigation of Compressive-sensing Image Reconstruction from Flying-focal-spot CT Data. IEEE Nucl Sci Symp Conf Rec (1997). 2009 Jan 01;2009:3458-3462. doi: 10.1109/NSSMIC.2009.5401787. PMID: 21318098; PMCID: PMC3037815.
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Cell-based Models for Discovery of Pharmacogenomic Markers of Anticancer Agent Toxicity.

Trends in cancer research

Zhang W, Huang RS, Dolan ME.
PMID: 21499559
Trends Cancer Res. 2008;4:1-13.

The field of pharmacogenomics is challenging because of the multigenic nature of drug response and toxicity. The candidate gene approach has been traditionally utilized to determine the contribution of genetic variation to a particular phenotype; however, the sequencing of...

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Zhang W, Huang RS, Dolan ME. Cell-based Models for Discovery of Pharmacogenomic Markers of Anticancer Agent Toxicity. Trends Cancer Res. 2008;4:1-13
Zhang, W., Huang, R. S., & Dolan, M. E. (2008). Cell-based Models for Discovery of Pharmacogenomic Markers of Anticancer Agent Toxicity. Trends in cancer research, 41-13.
Zhang, Wei, et al. "Cell-based Models for Discovery of Pharmacogenomic Markers of Anticancer Agent Toxicity." Trends in cancer research vol. 4 (2008): 1-13.
Zhang W, Huang RS, Dolan ME. Cell-based Models for Discovery of Pharmacogenomic Markers of Anticancer Agent Toxicity. Trends Cancer Res. 2008;4:1-13. PMID: 21499559; PMCID: PMC3076057.
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Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls.

Journal of medical imaging (Bellingham, Wash.)

Li H, Giger ML, Lan L, Janardanan J, Sennett CA.
PMID: 26158050
J Med Imaging (Bellingham). 2014 Oct;1(3):031009. doi: 10.1117/1.JMI.1.3.031009. Epub 2014 Nov 13.

We statistically compare the contributions of parenchymal phenotypes to mammographic density in distinguishing between high-risk cases and low-risk controls. The age-matched evaluation included computerized mammographic assessment of breast percent density (PD) and parenchymal patterns (phenotypes of coarseness and contrast)...

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Li H, Giger ML, Lan L, et al. Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls. J Med Imaging (Bellingham). 2014;1(3):031009doi: 10.1117/1.JMI.1.3.031009.
Li, H., Giger, M. L., Lan, L., Janardanan, J., & Sennett, C. A. (2014). Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls. Journal of medical imaging (Bellingham, Wash.), 1(3), 031009. https://doi.org/10.1117/1.JMI.1.3.031009
Li, Hui, et al. "Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls." Journal of medical imaging (Bellingham, Wash.) vol. 1,3 (2014): 031009. doi: https://doi.org/10.1117/1.JMI.1.3.031009
Li H, Giger ML, Lan L, Janardanan J, Sennett CA. Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls. J Med Imaging (Bellingham). 2014 Oct;1(3):031009. doi: 10.1117/1.JMI.1.3.031009. Epub 2014 Nov 13. PMID: 26158050; PMCID: PMC4478852.
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Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm.

Tsinghua science and technology

Bian J, Xia D, Sidky EY, Pan X.
PMID: 20617122
Tsinghua Sci Technol. 2010 Feb;15(1):68-73. doi: 10.1016/S1007-0214(10)70011-0.

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations,...

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Bian J, Xia D, Sidky EY, et al. Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm. Tsinghua Sci Technol. 2010;15(1):68-73doi: 10.1016/S1007-0214(10)70011-0.
Bian, J., Xia, D., Sidky, E. Y., & Pan, X. (2010). Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm. Tsinghua science and technology, 15(1), 68-73. https://doi.org/10.1016/S1007-0214(10)70011-0
Bian, Junguo, et al. "Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm." Tsinghua science and technology vol. 15,1 (2010): 68-73. doi: https://doi.org/10.1016/S1007-0214(10)70011-0
Bian J, Xia D, Sidky EY, Pan X. Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm. Tsinghua Sci Technol. 2010 Feb;15(1):68-73. doi: 10.1016/S1007-0214(10)70011-0. PMID: 20617122; PMCID: PMC2898485.
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Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?.

Inverse problems

Pan X, Sidky EY, Vannier M.
PMID: 20376330
Inverse Probl. 2009 Jan 01;25(12):1230009. doi: 10.1088/0266-5611/25/12/123009.

Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years-the reconstruction algorithm. Fundamental advances have been made in...

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Pan X, Sidky EY, Vannier M. Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?. Inverse Probl. 2009;25(12):1230009doi: 10.1088/0266-5611/25/12/123009.
Pan, X., Sidky, E. Y., & Vannier, M. (2009). Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?. Inverse problems, 25(12), 1230009. https://doi.org/10.1088/0266-5611/25/12/123009
Pan, Xiaochuan, et al. "Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?." Inverse problems vol. 25,12 (2009): 1230009. doi: https://doi.org/10.1088/0266-5611/25/12/123009
Pan X, Sidky EY, Vannier M. Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?. Inverse Probl. 2009 Jan 01;25(12):1230009. doi: 10.1088/0266-5611/25/12/123009. PMID: 20376330; PMCID: PMC2849113.
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Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.

Genome biology

Geeleher P, Cox NJ, Huang RS.
PMID: 27654937
Genome Biol. 2016 Sep 21;17(1):190. doi: 10.1186/s13059-016-1050-9.

We show that variability in general levels of drug sensitivity in pre-clinical cancer models confounds biomarker discovery. However, using a very large panel of cell lines, each treated with many drugs, we could estimate a general level of sensitivity...

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Geeleher P, Cox NJ, Huang RS. Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. Genome Biol. 2016;17(1):190doi: 10.1186/s13059-016-1050-9.
Geeleher, P., Cox, N. J., & Huang, R. S. (2016). Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. Genome biology, 17(1), 190. https://doi.org/10.1186/s13059-016-1050-9
Geeleher, Paul, et al. "Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models." Genome biology vol. 17,1 (2016): 190. doi: https://doi.org/10.1186/s13059-016-1050-9
Geeleher P, Cox NJ, Huang RS. Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. Genome Biol. 2016 Sep 21;17(1):190. doi: 10.1186/s13059-016-1050-9. PMID: 27654937; PMCID: PMC5031330.
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Showing 1 to 12 of 14 entries