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Acknowledgment: The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

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Research Funding:

This work was supported by Federal funds from the National Cancer Institute, National Institutes of Health (NIH) under Contract HHSN261200800001E, Contract 94995NBS23, Contract N01-CO-12400, and Contract 85983CBS43; by TCGA Contract 29X55193; by National Heart, Lung, and Blood Institute under Grant R24HL085343; by NIH under Grant U54 CA113001, Grant R01 CA86335, and Grant R01 CA116804; and NIH Public Health Service under Grant UL1 RR025008, Grant KL2 RR025009, or Grant TL1 RR025010 from the Clinical and Translational Science Awards program of National Center for Research Resources; by National Library of Medicine under Grant R01LM009239; and by Biomedical Information Science and Technology Initiative under Grant P20 EB000591.

Keywords:

  • Biology
  • brain tumor
  • image analysis
  • in silico
  • microscopy

An Integrative Approach for In Silico Glioma Research

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Journal Title:

IEEE Transactions on Biomedical Engineering

Volume:

Volume 57, Number 10

Publisher:

, Pages 2617-2621

Type of Work:

Article | Post-print: After Peer Review

Abstract:

The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors, where the morphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.

Copyright information:

© 2010 IEEE

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