Data and Society Lecture 3: Data and Healthbermaf/Data Course 2017/L3 - 2017.pdf · 2017. 2. 3. ·...

Fran Berman, Data and Society, CSCI 4967/6963

Data and Society Lecture 3: Data and Health

2/3/17

PDB, NIH, Precision Medicine


Announcements 2/3

• No Wednesday class February 8

• Op-Ed draft due February 10 – instructions

in Lecture 1 and Syllabus

• Research paper instructions next week

(Friday, February 10). No discussion article.


Today (2/3/17)

• Lecture 3: Data and Health

– Data and Community: PDB

– Data and Government: NIH BD2K

– Data and the Individual: Precision Medicine

• Discussion

• Break

• 4 Student Presentations

3


Wednesday Section Friday lecture

First Half of Class Second Half of Class Assignments

January 18 : NO class January 20 L!: Class Intro + Logistics / Survey / Digital Data in the 21st Century

Presentation Model / Op-Ed Instructions

January 25: NO class January 27 L2: Big data applications / Data and the election; Data and Target; Discussion

4 Presentations

February 1: 6 presentations February 3

L3: Data and Health / PDB, Precision Medicine; Discussion

4 Presentations

February 8: NO class February 10 L4: Data and Science / Earthquakes, LHC; Paper Instructions

4 Presentations Op-Ed Draft Due

February 15: 6 presentations

February 17 L5: Data Cyberinfrastructure; Discussion

4 Presentations Op-Ed Draft Back

February 22: 6 presentations

February 24 L6: Data Stewardship and Data Preservation; Discussion

4 presentations Op-Ed Final Due

March 1: NO class March 3 NO class

March 8: 6 presentations March 10 L7: Data Futures – Internet of Things; Discussion

4 presentations Paper Draft Due

March 15: Spring Break March 17 Spring Break

March 22: NO class March 24 L8: Data rights and policy / U.S. and EU; Discussion

4 presentations

March 29: 6 presentations March 31 Op-Ed Pecha-Kucha Paper Draft Back

April 5: NO class April 7 NO class

April 12: 4 presentations April 14 Hilary Mason Guest Lecture 4 presentations Final Paper Due

April 19: 4 presentations April 21 L9: Data and Ethics; Discussion 4 presentations

April 26: 6 presentations April 28 Paper Pecha-Kucha


Lecture 3: Data and Health


Information technologies have revolutionized the Health Sciences • Electronic Medical Records

– Greater data accessibility, analysis, potential for effective use

• Better disease diagnosis and treatment

– Disease modeling and analysis, IT-guided surgery, electronic monitoring, etc.

• Better understanding of biological structure and function and fundamental science

– Mapping of the human genome, characterization of biological function and disease trajectory

• Personalized / precision medicine

– Monitoring, early prediction of risk and diagnosis, customized treatment

• Public health

– Better prediction and response of epidemics

– Better characterization of health risk and mitigation

• Etc., etc., etc.


• What genes are associated with cancer?

• What parts of the brain are responsible for Alzheimer’s?

• How do coral reefs evolve over time?

• Who is at greatest risk for sickle cell anemia?

• …

Health Sciences Research: Data a fundamental driver for greater discovery

Integration across multiple scales in the biosciences

Disciplinary

Databases Users

Data

Access

and Use

Data

Integration

Organisms

Organs

Cells

Atoms

Bio-

polymers

Organelles

Cell Biology

Anatomy

Physiology

Proteomics

Medicinal

Chemistry

Genomics

Image courtesy of Mark Miller


Potential of data-driven discovery coupled with many technical and societal challenges

Regulation and policy

• Who owns health information? Who has a right to see it?

• Can health information be used to make non-health decisions (insurance premiums, job eligibility)?

• What regulations should apply to mobile health applications?

Culture and practice

• How should clinicians team effectively with technology for better diagnosis, treatment and care?

• How and by whom should personalized health information be used? How do we protect the privacy of patients while promoting the public good?

• When should health-related information be a “competitive advantage”?


Potential of data-driven discovery coupled with many technical and societal challenges

Ethics

• Under what conditions is stem cell research, cloning, euthanasia, health monitoring, etc. acceptable? Does the availability of relevant data change this?

• Who is responsible for a misleading / incorrect data-driven diagnosis?

• How should sensitive data be used?

Enabling Infrastructure

• How do we make information most useful?

• How should we support data access and sharing?

• How can we ensure reproducibility of data-driven results?


Lecture Outline: Data-driven efforts in the Health Sciences

• Data and Community: PDB

• Data and Government: NIH BD2K

• Data and the Individual: Precision Medicine


Data and Community: The Protein Data Bank

Some information in this section courtesy of Phil Bourne and Helen Berman


What is the Protein Data Bank (PDB)?

• International repository and archival database for information about the 3D structure of large biological molecules (such as proteins and nucleic acids)

• Most major scientific journals, and some funding agencies (including the NIH) now require scientists to submit their structure data to the PDB.

• Provides free worldwide public access 24/7 to accurate protein data


About PDB

• PDB data downloaded > 500,000,000 times as of 2016

• PDB supports the development of standards for the representation, annotation, and validation of these structural data that are collected from different experimental methods.

• World-wide PDB (wwPDB.org) is a consortium of groups that host deposition, annotation, and distribution centers for PDB data and collaborate on PDB projects: – RCSB [Research Collaboratory for Structural Bioinformatics] PDB (US: Rutgers

and SDSC/UCSD)

– PDB Europe (PDBe, UK)

– PDB Japan

– BioMagResBank (US)

• U.S. RCSB team includes computer scientists, biologists, chemists, educators.


Outcomes: Broad use, new innovation and discovery

PDB has enabled

• Safe storage of protein data

• Molecular replacement models for structure determination

• “Parts list” for modeling

• Structure based drug design

• Protein structure classification

• Protein structure prediction


PDB community ~325,000 unique users per month from ~190 countries [2016]

Fran Berman, Data and Society, CSCI 4967/6963 From the 2013 PDB Annual Report http://www.rcsb.org/pdb/general_information/news_publications/annual_reports/annual_report_year_2013.pdf

Global, multi-sector resource: 113,000+ PDB searchable structures in 2016


(Electron Microscopy)

(Nuclear Magnetic Resonance)

From http://ac.els-cdn.com/S0959440X1630077X/1-s2.0-S0959440X1630077X-main.pdf?_tid=49d80f9c-d0f1-11e6-949c-00000aab0f6b&acdnat=1483364722_a5b81cad4469cedae4b5b5f293ce4913


Considerable human and technology infrastructure needed: Deposition and Annotation Pipelines

From http://ac.els-cdn.com/S0959440X1630077X/1-s2.0-S0959440X1630077X-main.pdf?_tid=49d80f9c-d0f1-11e6-949c-00000aab0f6b&acdnat=1483364722_a5b81cad4469cedae4b5b5f293ce4913


PDB History

1970s • Community discussions about

how to establish an archive of protein structures

• Cold Spring Harbor meeting in protein crystallography

• PDB established at Brookhaven (Oct 1971; 7 structures)

1980s • Number of structures increases

as technology improves • Community discussions about

requiring depositions • IUCr guidelines established • Number of structures deposited

increases

1990s • Structural genomics begins • PDB moves to RCSB PDB

2000s • WWPDB formed • 50,000th structure released

(April 2008)

2010’s • 40th Anniversary of PDB (2011) • 10th Anniversary of WWPDB (2013) • 2013 / rcsb.org:

– ~286,000 unique visitors per month from 190 countries

– 1,000,000 downloads of data from PDB archive per day

– 1.3 TB per month transferred – 10,000 downloads of mobile app

Information courtesy of Helen Berman and 2013 PDB Annual Report


PDB and Data Sharing

“ A very important factor in the growth of the PDB has been the change in attitudes

regarding data sharing.

In 1971, the incentives to deposit data in the PDB were very practical: by putting data

in the archive, depositors would ensure that the data would not get lost. The task of

distributing data resident on magnetic tapes to interested parties located around the

world became the job of the PDB. In spite of these conveniences, it was not the norm

to deposit data.

It wasn't until the 1980s that several community groups began to establish

guidelines for data sharing. Once published, the funding agencies and the journals

began to adopt these guidelines.

Today, structure deposition into the PDB is a prerequisite for publication in virtually

every journal. These scientific, technological, and cultural changes have driven the

continual growth of the PDB.”

The Future of the Protein Data Bank

From: “The Future of the Protein Data Bank” http://onlinelibrary.wiley.com/doi/10.1002/bip.22132/full


PDB Business Model

• Funding models for PDB vary for each PDB center in WWPDB

– Different funding cycles

– Different funding criteria

– Multiple agencies involved

• Costs

– Cost for structures 2013 is roughly $750M (~$75K/structure)

– Cost for archiving is roughly $10,000,000

• Sustainability: Multiple models explored

– Current models: multiple-agency, multi national funds

– Potential models: • Journal model – pay per structure

• Congressional appropriation (NCBI)

• International funding structure with strong community oversight, rolling tenure

– Charitable WWPDB Foundation being explored to support education, outreach, continued collaboration with respect to standards, and community meetings

Wellcome Trust, EU,

CCP4, BBSRC, MRC,

EMBL

BIRD-JST,

MEXT

NSF, NIGMS, DOE, NLM,

NCI, NINDS, NIDDK

NLM Information courtesy of Helen Berman and Phil Bourne.


Data and Government: NIH Biomedical Data Initiatives

• Open Science and Open Data

• BD2K

• ADDS and NLM

• U.S. Precision Medicine Initiative


Open Science: NIH’s PubMed and PubMed Central (“open papers”)

• PubMed is a search engine provided free to the public by NIH (National Library of Medicine).

– PubMed searches primarily the MEDLINE DB of references and abstracts on life sciences and biomed research publications

– Some but not all PubMed searches include full papers

– Some PubMed articles hosted in PubMed Central

• PubMed Central is a free digital repository that archives publicly accessible full-text scholarly articles.

• NIH Public Access Policy: Papers on research funded by the National Institutes of Health must be available to the public for free through PubMed Central within 12 months of publication.

http://upload.wikimedia.org/wikipedia/commons/f/fb/US-NLM-PubMed-Logo.svg


Open Data: NIH successfully used its “bully pulpit” to drive broader biomedical community data sharing


Big Data to Knowledge (BD2K) Initiative • Focus: Development of innovative

and transforming approaches as well as tools for making Big Data and data science a more prominent component of biomedical research

• Broad development of underlying data ecosystem for biomedical researchers:

– Standards, methods, tools, SW, new algorithms and approaches to further biomedical research

– Access to shareable biomedical data through technologies, approaches and policies that enable and facilitate data sharing, discoverability, management, curation and meaningful re-use

– Workforce development and training to support data-driven biomedical research


Focused BD2K Funding

• Targeted SW development: – Funding for SW and methods development that impact data management,

data compression, data provenance, data visualization, data privacy, metadata, etc.

• Resource Indexing – Focus on data and resource discovery, citation and access for data and

software for biomedical researchers.

• Enhancing Training – Workforce training and education. Focus is on development of materials,

MOOCS, short courses, career development, training coordination center.

• Centers of Excellence – 11 Centers of Excellence for Big Data Computing and one for Data

Coordination and Integration. Centers develop approaches, SW tools, resources and provide training to advance data-driven biomedical research.


Data Science at NIH

• 2014 - 2017: NIH created position of Associate Director of Data Science (ADDS) and created budget to support data science at NIH

• Key foci of “ADDS” Office included:

– Sustainability of digital assets

– Training of data-savvy biomedical workforce

– Innovation in the biomedical data science ecosystem

– Refining of NIH internal processes to make the most of data and data technologies

– Communication within and outside of the NIH

• ADDS projects included BD2K projects, development of an NIH data commons, internal NIH initiatives, speaker series, etc.

• Dr. Phil Bourne served as ADDS office Director.


National Library of Medicine Strategic Planning

• 2017: National Library of Medicine now continuing / evolving ADDS work in data and data science.

• Focus of current strategic planning is increase impact in the:

– Role of NLM in advancing biomedical discovery and translational science

– Role of NLM in supporting the public’s health (e.g. clinical systems, public health systems and services, personal health)

– Role of NLM in advancing data science, open science, and biomedical informatics

– Role of NLM in building collections to support discovery and health in the 21st century

Relevant topics:

• Standards

• Computing

• Physical plant

• Research

• Education

• Workforce

• User communities

• Partnerships

• International engagement

• Public health / health disparities, etc.


U.S. Precision Medicine Initiative

• Announced in 2015 State of the Union

• Involves NIH, NCI, FDA, Veterans’s Affairs, DoD, and other govt. agencies

• Launched with $215M investment in 2016 budget

• More info at https://www.whitehouse.gov/precision-medicine

https://www.whitehouse.gov/precision-medicine





Precision Medicine

• Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in environment, lifestyle and genes for each person.

• Precision medicine not new – transfusion patients matched with donors according to blood types, etc., but new sources of data, cost-efficiency of sequencing genomic data, and potential for more refined and successful treatments driving huge expansion of the area.

• Precision medicine powered by patient data. All inherent potential and challenges with data involved:

– Effective infrastructure and workflows

– Privacy and access

– Interoperability

– Accuracy and interpretation


Data and the Patient -- Larry Smarr at TedMed (16 min)

http://www.tedmed.com/talks/show?id=18018




Lecture Materials

• 2013 Protein Data Bank Annual Report http://www.rcsb.org/pdb/general_information/news_publications/annual_reports/annual_report_year_2013.pdf

• RCSB Protein Data Bank: A Resource for Chemical, Biochemical, and Structural Explorations of Large and Small Biomolecules, Journal of Chemical Education, http://pubs.acs.org/doi/pdf/10.1021/acs.jchemed.5b00404

• The archiving and dissemination of biological structure data, ScienceDirect, http://ac.els-cdn.com/S0959440X1630077X/1-s2.0-S0959440X1630077X-main.pdf?_tid=49d80f9c-d0f1-11e6-949c-00000aab0f6b&acdnat=1483364722_a5b81cad4469cedae4b5b5f293ce4913

• The Protein Data Bank, Nucleic Acids Research, 2000 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC102472/.

• The Future of the Protein Data Bank, Biopolymers, volume 99, http://onlinelibrary.wiley.com/doi/10.1002/bip.22132/full

• “The Protein Data Bank and lessons in Data Management,” Briefings in Bioinformatics, http://www.sdsc.edu/pb/papers/briefings04.pdf

• PDB: www.rcsb.org

• NIH websites: https://www.nih.gov/, https://datascience.nih.gov/bd2k, https://www.nlm.nih.gov/pubs/plan/strategic_planning.html

• Precision Medicine, Nature, http://www.nature.com/nature/journal/v537/n7619_supp/full/537S49a.html

• Precision Medicine Initiative, White House, https://www.whitehouse.gov/precision-medicine

http://www.rcsb.org/pdb/general_information/news_publications/annual_reports/annual_report_year_2013.pdf




http://pubs.acs.org/doi/pdf/10.1021/acs.jchemed.5b00404

http://pubs.acs.org/doi/pdf/10.1021/acs.jchemed.5b00404

http://ac.els-cdn.com/S0959440X1630077X/1-s2.0-S0959440X1630077X-main.pdf?_tid=49d80f9c-d0f1-11e6-949c-00000aab0f6b&acdnat=1483364722_a5b81cad4469cedae4b5b5f293ce4913


















http://www.ncbi.nlm.nih.gov/pmc/articles/PMC102472/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC102472/

http://onlinelibrary.wiley.com/doi/10.1002/bip.22132/full

http://onlinelibrary.wiley.com/doi/10.1002/bip.22132/full

http://www.sdsc.edu/pb/papers/briefings04.pdf

http://www.sdsc.edu/pb/papers/briefings04.pdf

http://www.rcsb.org/

https://www.nih.gov/

https://www.nih.gov/

https://datascience.nih.gov/bd2k

https://datascience.nih.gov/bd2k

https://www.nlm.nih.gov/pubs/plan/strategic_planning.html

https://www.nlm.nih.gov/pubs/plan/strategic_planning.html

http://www.nature.com/nature/journal/v537/n7619_supp/full/537S49a.html

http://www.nature.com/nature/journal/v537/n7619_supp/full/537S49a.html






Discussion

• “I had my DNA Picture Taken with Varying Results”, New York Times, http://www.nytimes.com/2013/12/31/science/i-had-my-dna-picture-taken-with-varying-results.html

http://www.nytimes.com/2013/12/31/science/i-had-my-dna-picture-taken-with-varying-results.html


















Presentation Articles February 10

• February 10: (data and science)

– “Crowdsourcing: For the Birds”, NY Times, http://www.nytimes.com/2013/08/20/science/earth/crowdsourcing-for-the-birds.html?pagewanted=2&contentCollection=Science&action=click&region=EndOfArticle&module=RelatedCoverage&pgtype=article [Dan S]

– “Digital Keys for Unlocking the Humanities’ Riches”, New York Times, http://www.nytimes.com/2010/11/17/arts/17digital.html?pagewanted=all&_r=0 [Bobby M]

– “African Elephant Numbers Plummet 30 Percent, Landmark Study Finds,” National Geographic, http://news.nationalgeographic.com/2016/08/wildlife-african-elephants-population-decrease-great-elephant-census/ [Deborah A]

– “Astronomers Characterize Wolf 1061 Planetary System“, Sci News, http://www.sci-news.com/astronomy/wolf-1061-planetary-system-04552.html [Eric L]

http://www.nytimes.com/2013/08/20/science/earth/crowdsourcing-for-the-birds.html?pagewanted=2&contentCollection=Science&action=click&region=EndOfArticle&module=RelatedCoverage&pgtype=article









http://www.nytimes.com/2010/11/17/arts/17digital.html?pagewanted=all&_r=0

http://www.nytimes.com/2010/11/17/arts/17digital.html?pagewanted=all&_r=0

http://news.nationalgeographic.com/2016/08/wildlife-african-elephants-population-decrease-great-elephant-census/
















http://www.sci-news.com/astronomy/wolf-1061-planetary-system-04552.html













Presentation Articles for February 15

• February 15 (data and science)

– “Big Data: Astronomical or Genomical?” PLOS Biology, http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195 [Patrick C]

– “Big data shows how what we buy affects endangered species,” Science Daily, https://www.sciencedaily.com/releases/2017/01/170104103604.htm [Priyanka K]

– “Neuroscience: Big brain, big data”, Scientific American, https://www.scientificamerican.com/article/neuroscience-big-brain-big-data/ [Stephen N]

– “The Australian Square Kilometre Array Pathfinder Finally Hits the Big Data Highway”, Phys. Org., https://phys.org/news/2017-01-australian-square-kilometre-array-pathfinder.html [James N]

– “Eat, Sleep, Repeat: Crowdsourcing the data of a baby’s typical day”, Discover, http://blogs.discovermagazine.com/citizen-science-salon/2016/12/14/eat-sleep-repeat-crowdsourcing-the-data-of-a-babys-typical-day/#.WHL2wFMrKpo [Aditya S]

‒ “Computer Science Technique Helps Astronomers Explore the Universe” Inside Science, https://www.insidescience.org/news/computer-science-technique-helps-astronomers-explore-universe [Brandon T]

http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195

http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195

https://www.sciencedaily.com/releases/2017/01/170104103604.htm

https://www.sciencedaily.com/releases/2017/01/170104103604.htm

https://www.scientificamerican.com/article/neuroscience-big-brain-big-data/









https://phys.org/news/2017-01-australian-square-kilometre-array-pathfinder.html













http://blogs.discovermagazine.com/citizen-science-salon/2016/12/14/eat-sleep-repeat-crowdsourcing-the-data-of-a-babys-typical-day/.WHL2wFMrKpo

























https://www.insidescience.org/news/computer-science-technique-helps-astronomers-explore-universe














Presentation Articles February 17

• February 17 (data and cyberinfrastructure)

– “Innovation in Asthma Research: Using Ethnography to Study a Global Health Problem”, Ethnography Matters, http://ethnographymatters.net/blog/2012/10/27/the-asthma-files/ [Lee C]

– “Biology: the big challenges of big data”, Nature, http://www.nature.com/nature/journal/v498/n7453/full/498255a.html [Grigory A]

– “Precision Medicine in the million genome era”, Genetic Engineering and Biotechnology News, http://www.genengnews.com/gen-articles/precision-medicine-research-in-the-million-genome-era/5944 [Noah W]

– “New Technologies Bring Marine Archaeology Treasures to Light”, The Guardian, https://www.theguardian.com/science/2016/dec/29/new-technologies-bring-marine-archaeology-treasures-to-light [Rachel K]

http://ethnographymatters.net/blog/2012/10/27/the-asthma-files/








http://www.nature.com/nature/journal/v498/n7453/full/498255a.html

http://www.nature.com/nature/journal/v498/n7453/full/498255a.html

http://www.genengnews.com/gen-articles/precision-medicine-research-in-the-million-genome-era/5944

















https://www.theguardian.com/science/2016/dec/29/new-technologies-bring-marine-archaeology-treasures-to-light

















No class Wednesday. Class next Friday.

• Next Friday: Data and Science ; Presentations

• No Discussion Article. Research paper instructions next Friday


Break


Presentations


Presentation Articles for February 3

• February 3: (data and health)

– “The 21st Century Cures Act: FDA Reforms Aim to Spur Innovation the Pharmaceutical, Medial Device and Health Research Sectors”, Lexology, http://www.lexology.com/library/detail.aspx?g=fa622c15-f2c4-4397-9d64-6cd341fcaf3f [Andrea L]

– “Four Steps to Precision Public Health”, Nature, http://www.nature.com/news/four-steps-to-precision-public-health-1.21089 [Kusuma B]

‒ “Can IBM’s Watson do it all?”, Fast Company, https://www.fastcompany.com/3065339/mind-and-machine/can-ibms-watson-do-it-all [Erica B]

‒ “mHealth’s Year in Review: From Texting to Wearables to Telehealth’s Tricks (and Treats)”, mHealth Intelligence, http://mhealthintelligence.com/news/mhealths-year-in-review-from-texting-to-wearables-to-telehealths-tricks-and [Tim T]

http://www.lexology.com/library/detail.aspx?g=fa622c15-f2c4-4397-9d64-6cd341fcaf3f









http://www.nature.com/news/four-steps-to-precision-public-health-1.21089













https://www.fastcompany.com/3065339/mind-and-machine/can-ibms-watson-do-it-all
















http://mhealthintelligence.com/news/mhealths-year-in-review-from-texting-to-wearables-to-telehealths-tricks-and
























Data and Society Lecture 3: Data and Healthbermaf/Data Course 2017/L3 - 2017.pdf · 2017. 2. 3. ·...

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