Error message

  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Notice: A non well formed numeric value encountered in _hierarchical_select_dropbox_generate() (line 2168 of /home/bmihub/public_html/sites/all/modules/contributed/hierarchical_select/hierarchical_select.module).
  • Deprecated function: The each() function is deprecated. This message will be suppressed on further calls in menu_set_active_trail() (line 2404 of /home/bmihub/public_html/includes/menu.inc).
Leave blank for all. Otherwise, the first selected term will be the default instead of "Any".

  • Pan-cancer evaluation of clinical value of mitotic network activity index (MNAI) and its predictive value of immunotherapy

    Submitted by Stone on Tue, 01/31/2023 - 13:59

    Introduction

    Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. This project contains data related to our pan-cancer assessement of mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network.

    Related Publication

    To be added.

    Project category: 
    Data

    Downloads

    Version Download Date
    1.0 zip (1.58 MB) 2023-Jan-31

  • Chest CT based Imaging Biomarkers for Early Stage COVID-19 Screening

    Submitted by Stone on Mon, 06/01/2020 - 19:45

    Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and the early screening of COVID-19 is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening.

    Project category: 
    Source Code / Software

    Downloads

    Version Download Date
    2.0 zip (51.03 MB) 2020-Jul-01
    1.0 zip (51.02 MB) 2020-Jun-02

  • WBC Profiler

    Submitted by Stone on Sun, 12/23/2018 - 11:03

    The characterization and classification of white blood cells (WBC) is critical for the diagnosis of anemia, leukemia and many other hematologic diseases. WBC Profiler is a software (matlab-based) package based on unsupervised feature learning for efficient and effective WBC characterization and recognition.

    Project category: 
    Source Code / Software

    Downloads

    Version Download Date
    1.0 zip (1.86 GB) 2018-Dec-29

  • TCGA KIRC Cohort Cellular Morphometric Context Summarization

    Submitted by Hang Chang on Fri, 04/14/2017 - 09:49

    The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.

    In this project, we aims to provide cellular morphometric context summarization for TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Cohort, which can be further utilized for various end points, such as,

    • integrated analysis with OMIC data; and
    • construction of predictive models of clinical outcome.

    Examples can be found in our previous publication:

    • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

    Visualization Tool:  KIRC Whole Slide Images (WSI) and the corresponding segmentation results.

    Project category: 
    Data

    Downloads

    Version Download Date
    1.0 zip (5.85 MB) 2017-Apr-14

  • TCGA KIRC Cohort Cellular Morphometric Univariate Summarization

    Submitted by Hang Chang on Fri, 04/14/2017 - 09:37

    The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.

    In this project, we aims to provide univariate cellular morphoetric summarization for TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Cohort, which can be further utilized for various end points, such as,

    • integrated analysis with OMIC data;
    • construction of predictive models of clinical outcome

    Examples can be found in our previous publication:

    • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

    Visualization Tool:  KIRC Whole Slide Images (WSI) and the corresponding segmentation results.

    Project category: 
    Data

    Downloads

    Version Download Date
    1.0 zip (99.53 MB) 2017-Apr-14

  • TCGA GBM LGG Cohorts Joint Cellular Morphometric Context Summarization

    Submitted by Hang Chang on Tue, 02/21/2017 - 12:45

    The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.

    In this project, we aims to provide joint cellular morphometric context summarization for TCGA Glioblastoma (GBM) and lower grade glioma (LGG) cohorts, which can be further utilized for various end points, such as,

    • integrated analysis with OMIC data; and
    • construction of predictive models of clinical outcome.

    Examples can be found in our previous publication:

    • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

    Visualization Tool:  GBM Whole Slide Images (WSI) and the corresponding segmentation results.

    Visualization Tool:  LGG Whole Slide Images (WSI) and the corresponding segmentation results.

    Project category: 
    Data

    Downloads

    Version Download Date
    1.0 zip (6.01 MB) 2017-Feb-21

  • Transcriptomic analysis of mammary tumors from MMTV-ErbB2 transgenic mice

    Submitted by Jian-Hua Mao on Thu, 10/13/2016 - 12:34

    The tyrosine kinase ErbB2 positive breast tumors have more aggressive tumor growth, poorer clinical outcome, and more resistance to radiotherapy, chemotherapy and hormone therapy. A humanized anti-ErbB2 monoclonal antibody Herceptin and a small molecules inhibitor Lapatinib were developed and approved by FDA to treat patients with ErbB2 amplification and overexpression. Unfortunately, most ErbB2+ breast cancers do not respond to Herceptin and Lapatinib, and the majority of responders become resistant within 12 months of initial therapy (defined as secondary drug resistance). Such differences in response to Lapatinib treatment is contributed by substantial heterogeneity within ErbB2+ breast cancers. To address this possibility, we carried out transcriptomic analysis of mammary tumors from genetically diverse MMTV-ErbB2 mice. This will help us to have a better understanding of the heterogeneous response to ErbB2 targeted therapy and permit us to design better and more individualized (personalized) treatment strategies for human ErbB2 positive breast cancer.

    Project category: 
    Data

  • Expression profiling reveals transcriptional regulation by Fbxw7/mTOR pathway in radiation-induced mouse thymic lymphomas

    Submitted by Jian-Hua Mao on Thu, 10/13/2016 - 12:23

    We used gene transcript profiling to gain a deeper understanding of the role of FBXW7 in tumor development and to determine the influence of mTOR inhibition by rapamycin on tumor transcriptome and biological functions. In comparison to tumors from p53 single heterozygous (p53+/-) mice, we find that tumors from Fbxw7/p53 double heterozygous (Fbxw7+/-p53+/-) mice show significant deregulation of cholesterol metabolic processes independent of rapamycin treatment, while cell cycle related genes were upregulated in tumors from placebo treated Fbxw7+/-p53+/- mice, but not in tumors from rapamycin treated Fbxw7+/-p53+/- mice. On the other hand, tumors from rapamycin treated Fbxw7+/-p53+/- mice were enriched for genes involved in the integrated stress response, an adaptive mechanism to survive in stressful environments.

    Project category: 
    Data

  • BioQuant Virtual Machine

    Submitted by Hang Chang on Thu, 04/28/2016 - 20:52

    BioQuant is a software, initially developed by the Imaing and Informatics Lab at Lawrence Berkeley National Laboratory, for large-scale high performance quantification of macromolecules involved in cell-cell adhesion, genomic instability, and DNA repair proteins, etc., which has been widely used by members at the Life Sciences Division of Lawrence Berkeley National Laboratory for scientific discoveries.

    Project category: 
    Source Code / Software

    Downloads

    Version Download Date
    1.0 ova (3.85 GB) 2016-May-29

  • Iterative Radial Voting

    Submitted by Hang Chang on Tue, 04/12/2016 - 11:54

    Iterrative Radial Voting is an advanced image processing algorithm, initially developed by the Imaing and Informatics Lab at Lawrence Berkeley National Laboratory for the inference of structural saliency and characterization of subcellular events. An example of kernel topology and radial object localization is illustrated as follows,

    Project category: 
    Source Code / Software

    Downloads

    Version Download Date
    1.0 zip (7.9 MB) 2016-Apr-12

  • Stacked Predictive Sparse Decomposition

    Submitted by Hang Chang on Wed, 03/09/2016 - 10:57

    StackedPSD pileline

    Stacked Predictive Sparse Decomposition is a machine learning algorithm that was initially developed for the classification of distinct regions of microanatomy and histopathology. This project contains the source code (matlab) for Stacked Predictive Sparse Decomposition that is described in the publication as follows,

    • Hang Chang*, Yin Zhou*, A Borowsky, K Barner, Paul Spellman and Bahram Parvin. “Stacked Predictive Sparse Decomposition for Classification of Histology Sections.”International Journal of Computer Vision (Special Issues on Deep Learning), (2015): 113(1) 3-18.(*Co-First Authors)
    Project category: 
    Source Code / Software

    Downloads

    Version Download Date
    1.0 zip (14.05 MB) 2016-Mar-09

  • Multispectral Convolutional Sparse Coding

    Submitted by Hang Chang on Tue, 03/08/2016 - 14:08

    Multispectral Convolutional Sparse Coding is a machine learning algorithm that was initially developed for the classification of distinct regions of microanatomy and histopathology. This project contains the source code (matlab) for Multispectral Convolutional Sparse Coding that is described in the publication as follows,

    • Yin Zhou*, Hang Chang*, Kenneth Barner, Paul Spellman, and Bahram Parvin. “Classification of Histology Sections via Multispectral Convolutional Sparse Coding.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, Ohio, U.S, June 2014. (*Co-First Authors)
    Project category: 
    Source Code / Software

    Downloads

    Version Download Date
    1.0 zip (43.31 MB) 2016-Mar-09

  • TCGA LGG Cohort Cellular Morphometric Context Summarization

    Submitted by Hang Chang on Sat, 03/05/2016 - 20:32

    The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.

    In this project, we aims to provide cellular morphometric context summarization for TCGA lower grade glioma (LGG) Cohort, which can be further utilized for various end points, such as,

    • integrated analysis with OMIC data; and
    • construction of predictive models of clinical outcome.

    Examples can be found in our previous publication:

    • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

    Visualization Tool:  LGG Whole Slide Images (WSI) and the corresponding segmentation results.

    Project category: 
    Data

    Downloads

    Version Download Date
    2.0 zip (7.06 MB) 2017-Feb-21
    1.0 zip (7.07 MB) 2016-Mar-08

  • TCGA LGG Cohort Cellular Morphometric Univariate Summarization

    Submitted by Hang Chang on Sat, 03/05/2016 - 20:30

    The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.

    In this project, we aims to provide univariate cellular morphoetric summarization for TCGA lower grade glioma (LGG) Cohort, which can be further utilized for various end points, such as,

    • integrated analysis with OMIC data;
    • construction of predictive models of clinical outcome

    Examples can be found in our previous publication:

    • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

    Visualization Tool:  LGG Whole Slide Images (WSI) and the corresponding segmentation results.

    Project category: 
    Data

    Downloads

    Version Download Date
    1.0 zip (62.17 MB) 2016-Mar-05

  • TCGA GBM Cohort Cellular Morphometric Context Summarization

    Submitted by Hang Chang on Fri, 03/04/2016 - 20:02

    The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.

    In this project, we aims to provide cellular morphometric context summarization for TCGA Glioblastoma (GBM) Cohort, which can be further utilized for various end points, such as,

    • integrated analysis with OMIC data; and
    • construction of predictive models of clinical outcome.

    Examples can be found in our previous publication:

    • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

    Visualization Tool:  GBM Whole Slide Images (WSI) and the corresponding segmentation results.

    Project category: 
    Data

    Downloads

    Version Download Date
    2.0 zip (7.4 MB) 2017-Feb-21
    1.0 zip (7.35 MB) 2016-Mar-05

  • TCGA GBM Cohort Cellular Morphometric Univariate Summarization

    Submitted by Hang Chang on Thu, 03/03/2016 - 15:34

    The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.

    In this project, we aims to provide univariate cellular morphoetric summarization for TCGA Glioblastoma (GBM) Cohort, which can be further utilized for various end points, such as,

    • integrated analysis with OMIC data;
    • construction of predictive models of clinical outcome

    Examples can be found in our previous publication:

    • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

    Visualization Tool:  GBM Whole Slide Images (WSI) and the corresponding segmentation results.

    Project category: 
    Data

    Downloads

    Version Download Date
    1.0 zip (73.64 MB) 2016-Mar-05