Main Page - Revision history

Guergana at 20:16, 9 April 2019

2019-04-09T20:16:22Z

JamesM: add link to DeepPhe google group

2018-11-02T10:01:32Z

add link to DeepPhe google group

JamesM: add News and link to DeepPhe on github - 0.2.0 released

2018-11-02T09:59:54Z

add News and link to DeepPhe on github - 0.2.0 released

Healthnlp at 15:02, 19 August 2016

2016-08-19T15:02:52Z

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 '''
-Visit the [https://github.com/DeepPhe/DeepPhe-Release/blob/master/README.md DeepPhe project] on github to download the software for DeepPhe 0.2.0.
+Visit the [https://github.com/DeepPhe/DeepPhe-Release/blob/master/README.md DeepPhe project] on github to download the software.
 For questions, contact us via the [https://groups.google.com/forum/#!forum/deepphe DeepPhe group]

← Older revision		Revision as of 10:01, 2 November 2018
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	Visit the [https://github.com/DeepPhe/DeepPhe-Release/blob/master/README.md DeepPhe project] on github to download the software for DeepPhe 0.2.0.		Visit the [https://github.com/DeepPhe/DeepPhe-Release/blob/master/README.md DeepPhe project] on github to download the software for DeepPhe 0.2.0.
		+
		+	For questions, contact us via the [https://groups.google.com/forum/#!forum/deepphe DeepPhe group]

← Older revision		Revision as of 09:59, 2 November 2018
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	We propose the advanced development and extension of a software platform for performing deep phenotype extraction directly from medical records of patients with cancer, with the goal of enabling translational cancer research and [https://en.wikipedia.org/wiki/Precision_medicine precision medicine]. The work builds on previous informatics research and software development efforts from Boston Children’s Hospital and University of Pittsburgh groups, both individually and together. Multiple software projects developed by our groups (some initially funded by NCI) that have already passed the initial prototyping and pilot development phase ([https://emerge.mc.vanderbilt.edu/about-emerge/ eMERGE], [https://clear.colorado.edu/TemporalWiki/index.php/Main_Page THYME], [http://ties.dbmi.pitt.edu/ TIES], ODIE, [http://ctakes.apache.org/ Apache cTAKES]) will be combined and extended to produce an advanced software platform for accelerating cancer research. Previous work in a number of NIH-funded [https://en.wikipedia.org/wiki/Translational_science translational science] initiatives has already demonstrated the benefits of these methodologies (e.g. [https://emerge.mc.vanderbilt.edu/about-emerge/ Electronic Medical Record and Genomics (eMERGE)], [http://www.pgrn.org/ PharmacoGenomics Research Network (PGRN)], [http://informatics.mayo.edu/sharp/index.php/Main_Page SHARPn], [https://www.i2b2.org/ i2b2]). However, to date these initiatives have focused exclusively on select non-cancer phenotypes and have had the goal of dichotomizing patients for a particular phenotype of interest (for example, [https://en.wikipedia.org/wiki/Diabetes_mellitus_type_2 Type II Diabetes], [https://en.wikipedia.org/wiki/Rheumatoid_arthritis Rheumatoid Arthritis], or Multiple Sclerosis). In contrast, our proposed work focuses on extracting and representing multiple phenotype features for individual patients, to build a cancer phenotype model, relating observable traits over time for individual patients.		We propose the advanced development and extension of a software platform for performing deep phenotype extraction directly from medical records of patients with cancer, with the goal of enabling translational cancer research and [https://en.wikipedia.org/wiki/Precision_medicine precision medicine]. The work builds on previous informatics research and software development efforts from Boston Children’s Hospital and University of Pittsburgh groups, both individually and together. Multiple software projects developed by our groups (some initially funded by NCI) that have already passed the initial prototyping and pilot development phase ([https://emerge.mc.vanderbilt.edu/about-emerge/ eMERGE], [https://clear.colorado.edu/TemporalWiki/index.php/Main_Page THYME], [http://ties.dbmi.pitt.edu/ TIES], ODIE, [http://ctakes.apache.org/ Apache cTAKES]) will be combined and extended to produce an advanced software platform for accelerating cancer research. Previous work in a number of NIH-funded [https://en.wikipedia.org/wiki/Translational_science translational science] initiatives has already demonstrated the benefits of these methodologies (e.g. [https://emerge.mc.vanderbilt.edu/about-emerge/ Electronic Medical Record and Genomics (eMERGE)], [http://www.pgrn.org/ PharmacoGenomics Research Network (PGRN)], [http://informatics.mayo.edu/sharp/index.php/Main_Page SHARPn], [https://www.i2b2.org/ i2b2]). However, to date these initiatives have focused exclusively on select non-cancer phenotypes and have had the goal of dichotomizing patients for a particular phenotype of interest (for example, [https://en.wikipedia.org/wiki/Diabetes_mellitus_type_2 Type II Diabetes], [https://en.wikipedia.org/wiki/Rheumatoid_arthritis Rheumatoid Arthritis], or Multiple Sclerosis). In contrast, our proposed work focuses on extracting and representing multiple phenotype features for individual patients, to build a cancer phenotype model, relating observable traits over time for individual patients.
		+
		+	'''News - DeepPhe v0.2.0 Released
		+	'''
		+
		+	Visit the [https://github.com/DeepPhe/DeepPhe-Release/blob/master/README.md DeepPhe project] on github to download the software for DeepPhe 0.2.0.

@@ Line 1: / Line 1: @@
 '''Welcome to the Cancer Deep Phenotype Extraction (DeepPhe) project.
 '''
 Cancer is a genomic disease, with enormous heterogeneity in its behavior. In the past, our methods for categorization, prediction of outcome, and treatment selection have relied largely on a morphologic classification of cancer. But new technologies are fundamentally reframing our views of cancer initiation, progression, [https://en.wikipedia.org/wiki/Metastasis metastasis], and response to treatment; moving us towards a molecular classification of cancer. This transformation depends not only on our ability to deeply investigate the cancer genome, but also on our ability to link these specific molecular changes to specific tumor behaviors. As sequencing costs continue to decline at a supra-[https://en.wikipedia.org/wiki/Moore%27s_law Moore’s law] rate, a torrent of cancer genomic data is looming. However, our ability to deeply investigate the cancer genome is outpacing our ability to correlate these changes with the phenotypes that they produce. [https://en.wikipedia.org/wiki/Translational_science Translational investigators] seeking to associate specific genetic, epigenetic, and systems changes with particular tumor behaviors, lack access to detailed observable traits about the cancer (the so called ‘deep phenotype’), which has now become a major barrier to research.
 We propose the advanced development and extension of a software platform for performing deep phenotype extraction directly from medical records of patients with cancer, with the goal of enabling translational cancer research and [https://en.wikipedia.org/wiki/Precision_medicine precision medicine]. The work builds on previous informatics research and software development efforts from Boston Children’s Hospital and University of Pittsburgh groups, both individually and together. Multiple software projects developed by our groups (some initially funded by NCI) that have already passed the initial prototyping and pilot development phase ([https://emerge.mc.vanderbilt.edu/about-emerge/ eMERGE], [https://clear.colorado.edu/TemporalWiki/index.php/Main_Page THYME], [http://ties.dbmi.pitt.edu/ TIES], ODIE, [http://ctakes.apache.org/ Apache cTAKES]) will be combined and extended to produce an advanced software platform for accelerating cancer research. Previous work in a number of NIH-funded [https://en.wikipedia.org/wiki/Translational_science translational science] initiatives has already demonstrated the benefits of these methodologies (e.g. [https://emerge.mc.vanderbilt.edu/about-emerge/ Electronic Medical Record and Genomics (eMERGE)], [http://www.pgrn.org/ PharmacoGenomics Research Network (PGRN)], [http://informatics.mayo.edu/sharp/index.php/Main_Page SHARPn], [https://www.i2b2.org/ i2b2]). However, to date these initiatives have focused exclusively on select non-cancer phenotypes and have had the goal of dichotomizing patients for a particular phenotype of interest (for example, [https://en.wikipedia.org/wiki/Diabetes_mellitus_type_2 Type II Diabetes], [https://en.wikipedia.org/wiki/Rheumatoid_arthritis Rheumatoid Arthritis], or Multiple Sclerosis). In contrast, our proposed work focuses on extracting and representing multiple phenotype features for individual patients, to build a cancer phenotype model, relating observable traits over time for individual patients.