HealthNLP-Deepphe - User contributions [en]

Main Page

2019-04-09T20:16:22Z

Guergana:

'''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.

'''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 questions, contact us via the [https://groups.google.com/forum/#!forum/deepphe DeepPhe group]

Licensing

2019-04-09T20:15:05Z

Guergana: /* Licensing of Software */

===Goals===

* Encourage the broad use of DeepPhe tools and models, while maintaining openness and attribution.
* Comply with resource sharing plan as submitted to NIH. See, for example [ITCR policies](http://itcr.nci.nih.gov/about-itcr)

===Distinction===

We are reading "software" as referring to executable components, while "models" refers to the OWL data models developed for representation of cancer phenotypes.

===Licensing of Software===

* cTAKES is licensed under the Apache Source License 2.0
* DeepPhe is licensed under a dual license -- free for non-profits and for a fee for for-profits, see DeepPhe repository at https://github.com/DeepPhe/DeepPhe-Release

===Licensing of Other Models===

* List of non-software licenses -- https://en.wikipedia.org/wiki/List_of_free_content_licenses
** Licensing for content are appropriate
* Principles
** receive credit for what we do
** allow other people to contribute (not just take it and use it) -> share alike
* Creative Commons
** Creative Commons does not recommend the use of Creative Commons licenses for software.
** https://en.wikipedia.org/wiki/Creative_Commons_license
** CC BY 2.0 - https://creativecommons.org/licenses/by/2.0/
*** you are free to share and adapt when you give attribution (You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.)
** CC BY 3.0 - http://creativecommons.org/licenses/by/3.0/
** is it compatible with Apache license?
*** CC BY 2.5 and 3.0 are in Category B - http://www.apache.org/legal/resolved.html
*** Unmodified media under the Creative Commons Attribution-Share Alike 2.5 and Creative Commons Attribution-Share Alike 3.0 licenses may be included in Apache products, subject to the licenses attribution clauses which may require LICENSE/NOTICE/README changes. For any other type of CC-SA licensed work, please contact the Legal PMC.
** CY BY 4.0 http://creativecommons.org/licenses/by/4.0/
*** is it compatible with the Apache license? We believe so.. see [this thread](http://apache.markmail.org/search/?q=legal-discuss#query:legal-discuss%20order%3Adate-backward+page:1+mid:3whmjdfqnvukcg2s+state:results)
* Open Content
** https://en.wikipedia.org/wiki/Open_Content_License -- The Open Content License is a share-alike public copyright license which can be applied to a work to make it open content. This license is not compatible with any other license in that it requires derivative works to be licensed under the Open Content License.
** probably not compatible with Apache.
* Art Libre
** https://en.wikipedia.org/wiki/Free_Art_License - a copyleft license that grants the right to freely copy, distribute, and transform creative works without the author's explicit permission

===DeepPhe Ontology Models===

* DeepPhe Ontology models will be licensed using CC-BY 4.0
* This is consistent with plans to use CC-BY 4.0 to license the schema ontology upon which the DeepPhe model is based.

Funding

2019-04-09T20:13:42Z

Guergana:

[[File:ITCR_Horizontal.png|270px|right]]
The project described is supported by the National Cancer Institute at the US National Institutes of Health. It is part of the NCI's Informatics Technology for Cancer Research ([http://itcr.nci.nih.gov/ ITCR]) Initiative. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Goals

2019-04-09T20:12:23Z

Guergana:

Our aims support the development and evaluation of novel methods for cancer deep phenotype extraction:

'''Specific Aim 1''': Develop methods for extracting phenotypic profiles

'''Specific Aim 2''': Extract gene/protein mentions and their variants from the clinical narrative

'''Specific Aim 3''': Create longitudinal representation of disease process and its resolution

[[File:Aim 3.jpg| center]]

'''Specific Aim 5''': Design and implement a computational platform for deep phenotype discovery and analytics for translational investigators, including integrative visual analytics.

[[File:Architecture overview.png| center | 700px]]

'''Specific Aim 6''': Advance translational research in driving cancer biology research projects in breast cancer, ovarian cancer, and melanoma. Include research community throughout the design of the platform and its evaluation. Disseminate freely available software.

'''''Impact:''''' The proposed work will produce novel methods for extracting detailed phenotype information directly from the EMR, the major source of such data for patients with cancer. Extracted phenotypes will be used in three ongoing translational studies with a precision medicine focus. Dissemination of the software will enhance the ability of cancer researchers to abstract meaningful clinical data for translational research. If successful, systematic capture and representation of these phenotypes from EMR data could later be used to drive clinical genomic decision support.