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Author Notes:

Contributor Information: Jun Kong, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

Lee A.D. Cooper, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

Fusheng Wang, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

David A. Gutman, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

Jingjing Gao, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

Candace Chisolm, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

Ashish Sharma, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA. Tony Pan, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA. Erwin G. Van Meir, Department of Neurosurgery and Hematology and Medical Oncology, School of Medicine and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. Tahsin M. Kurc, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA. Carlos S. Moreno, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

Joel H. Saltz, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

Daniel J. Brat, Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322, USA.

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Research Funding:

This work was supported by the NCI Contract HHSN261200800001E; TCGA Contract 29 x 55193; NIH 5R01LM009239-04; NHLBI R24 HL085343; and by the Clinical and Translational Science Awards program under PHS Grant UL1RR025008.

Keywords:

  • Glioblastoma
  • multi-modal data process
  • in silico
  • cluster analysis
  • translational integration

Integrative, Multi-modal Analysis of Glioblastoma Using TCGA Molecular Data, Pathology Images and Clinical Outcomes

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Journal Title:

IEEE Transactions on Information Technology in Biomedicine

Volume:

Volume 58, Number 12

Publisher:

, Pages 3469-3474

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Multi-modal, multi-scale data synthesis is becoming increasingly critical for successful translational biomedical research. In this paper, we present a large-scale investigative initiative on glioblastoma, a high-grade brain tumor, with complementary data types using in silico approaches. We integrate and analyze data from The Cancer Genome Atlas Project on glioblastoma that includes novel nuclear phenotypic data derived from microscopic slides, genotypic signatures described by transcriptional class and genetic alterations, and clinical outcomes defined by response to therapy and patient survival. Our preliminary results demonstrate numerous clinically and biologically significant correlations across multiple data types, revealing the power of in silico multi-modal data integration for cancer research.
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