Sage Bionetworks is launching a new platform called BRIDGE to enable open collaboration between patients, researchers, and funders. [1] BRIDGE will allow disease communities to define and contribute to research projects. [2] The goal is to involve citizen-patients more directly in research by allowing them to consent to participate, take surveys, share data, and participate in games and crowdsourcing challenges. [3] This could help shift biomedical research to be more open, collaborative, and relevant.
Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Inte...Amit Sheth
Ora Lassila and Amit Sheth, "Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Interoperability", Invited Talk at ONC-HHS Invitational Workshop on Next Generation Interoperability for Health, Washington DC, January 19-20, 2011.
Semantic Web for Health Care and Biomedical InformaticsAmit Sheth
Amit Sheth, "Semantic Web for Health Care and Biomedical Informatics," Keynote at NSF Biomed Web Workshop, Corbett, Oregon, December 4-5, 2007.
http://www.biomedweb.info/2007/
Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Inte...Amit Sheth
Ora Lassila and Amit Sheth, "Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Interoperability", Invited Talk at ONC-HHS Invitational Workshop on Next Generation Interoperability for Health, Washington DC, January 19-20, 2011.
Semantic Web for Health Care and Biomedical InformaticsAmit Sheth
Amit Sheth, "Semantic Web for Health Care and Biomedical Informatics," Keynote at NSF Biomed Web Workshop, Corbett, Oregon, December 4-5, 2007.
http://www.biomedweb.info/2007/
2016 Data Commons and Data Science Workshop June 7th and June 8th 2016. Genomic Data Commons, FAIR, NCI and making data more findable, publicly accessible, interoperable (machine readable), reusable and support recognition and attribution
NCI Cancer Genomics, Open Science and PMI: FAIR Warren Kibbe
Talk given to the NLM Fellows on July 8, 2016. Touches on Cancer Genomics, Open Science and PMI: FAIR in NCI genomics thinking and projects. Includes discussion of the Genomic Data Commons (GDC), Cancer Data Ecosystem, Data sharing, and the NCI cancer clinical trials open API.
May 2016 NCI Cancer Center Directors meeting. Data Sharing and the Cancer Genomic Data Commons (GDC). Focus is on cancer genomic and clinical phenotype data.
The current paradigm in the pharmaceutical industry is that products can only be created and developed by massive collaborative teams. Each company has to build their own costly R&D platforms and IT infrastructure. Other research industries realized decades ago that they had to share data and methods because of cost. The pharmaceutical industry has been slow to realize this. Expanding beyond our recent book (Collaborative Computational Technologies for Biomedical Research) in which a growing number of technologies, consortia, precompetitive initiatives and complex collaboration networks are described, we suggest a more open drug discovery is being enabled by collaborative computational technologies. Academia however, is not training the next generation of scientists to practice open science or even collaborate, this represents challenges and opportunities. We will describe our observations and make recommendations that impact everyone from technology developers to granting agencies. This may enable future discoveries to be made outside traditional institutions.
GenomeTrakr: Perspectives on linking internationally - Canada and IRIDA.cafionabrinkman
Talk at GenomeTrakr network meeting Sept 23 2015 in Washington DC. On Canada's open source Integrated Rapid Infectious Disease Analysis (IRIDA) bioinformatics platform - aiding genomic epidemiology analysis for public health agencies with planned open data release and linkage to GenomeTrakr. Discussed perspectives, challenges, solutions for getting more GenomeTrakr participation internationally.
2016 Data Commons and Data Science Workshop June 7th and June 8th 2016. Genomic Data Commons, FAIR, NCI and making data more findable, publicly accessible, interoperable (machine readable), reusable and support recognition and attribution
NCI Cancer Genomics, Open Science and PMI: FAIR Warren Kibbe
Talk given to the NLM Fellows on July 8, 2016. Touches on Cancer Genomics, Open Science and PMI: FAIR in NCI genomics thinking and projects. Includes discussion of the Genomic Data Commons (GDC), Cancer Data Ecosystem, Data sharing, and the NCI cancer clinical trials open API.
May 2016 NCI Cancer Center Directors meeting. Data Sharing and the Cancer Genomic Data Commons (GDC). Focus is on cancer genomic and clinical phenotype data.
The current paradigm in the pharmaceutical industry is that products can only be created and developed by massive collaborative teams. Each company has to build their own costly R&D platforms and IT infrastructure. Other research industries realized decades ago that they had to share data and methods because of cost. The pharmaceutical industry has been slow to realize this. Expanding beyond our recent book (Collaborative Computational Technologies for Biomedical Research) in which a growing number of technologies, consortia, precompetitive initiatives and complex collaboration networks are described, we suggest a more open drug discovery is being enabled by collaborative computational technologies. Academia however, is not training the next generation of scientists to practice open science or even collaborate, this represents challenges and opportunities. We will describe our observations and make recommendations that impact everyone from technology developers to granting agencies. This may enable future discoveries to be made outside traditional institutions.
GenomeTrakr: Perspectives on linking internationally - Canada and IRIDA.cafionabrinkman
Talk at GenomeTrakr network meeting Sept 23 2015 in Washington DC. On Canada's open source Integrated Rapid Infectious Disease Analysis (IRIDA) bioinformatics platform - aiding genomic epidemiology analysis for public health agencies with planned open data release and linkage to GenomeTrakr. Discussed perspectives, challenges, solutions for getting more GenomeTrakr participation internationally.
This presentation outlines a mechanism for using the power of "Big Data", social networking and technology infrastructure to speed the process of curing a horrible disease.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
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Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
4. What will it take to understand disease?
Biobanks
RNA, DNA and proteins
Moving beyond altered
components lists
5. What will it take to understand disease?
Driver Mutations
Modifier Genes
Environmental factors
Context dependencies
Co-Medications
Pharmacogenomic factors
State of the Immune System
7. How can we afford to get there?
Institutional Extensions
Foundational Walled Gardens
Academic Consortia
New Proprietary Data
Aggregators
8. Five Powerful Convergence Breakthroughs
Enable some Alternative Paths
1- Now possible to generate massive amount of human “omic’s” data
2-“Top Down” Network Modeling for Diseases are emerging
3- IT Infrastructure and Cloud compute capacity allows a generative open approach to
biomedical problem solving
4- Nascent Movement for patients to Control Private information allowing sharing
5- Open Social Media allowing citizens and experts to use gaming to solve problems
THESE FIVE TRENDS TOGETHER CAN ENABLE AN OPEN COMMUNITY OF IMPATIENT CITIZENS
-- AS PATIENTS/RESEARCHERS/FUNDERS
9. The Biomedical Information Commons Alternative
Collecting
Storing Data
DataBiomedicine
Information
Commons
Processing
Sharing Data
Data
Commons are resources that are owned in common or shared among communities.
-David Bollier
10. Components of the Biomedical Commons
Data
Generators Patients/
Citizens
CURATED
DATA Data
TOOLS/ Analysts
METHODS
RAW
DATA
Clinicians
ANALYZES/
MODELS
SYNAPSE
Experimentalists
11. Why Sage Bionetworks?
We believe in a world where biomedical research has changed. It
will be conducted in an open, collaborative way where each of us
can contribute to making better, faster, relevant discoveries
We enable others We activate
• Develop platforms for We perform research • Diverse collaborations with
collaboration and • Leading computational individuals/researchers and
engagement – Synapse, biology research institutions to grow the
BRIDGE • Novel training and biomedical Commons together
• Defining governance internship programs • Crowdsourcing approaches to
approaches– PLC challenge the communities
12. So…What is BRIDGE?
A place where patients, researchers and
funders can collaborate to define and
contribute to research in their, and other
disease, communities
An online platform we are defining with five
disease communities and their launch
projects
13. What will BRIDGE give us?
Changing the research dialogue Sharing of data and
Rich data from a
research with a wider
wide participant base
audience
A networked team to
Crowdsourcing
collaborate and
method of research
Really involving Citizen-Patients learn
14. TO
CONSENT
RESEARCH
BRIDGE
Education
Surverys/Forums
Data Use Tracking
Games
The six domains
Learning From Adjacent
Diseases
BRIDGE’s main components and interactions
Crowdsourcing
14
15. Synapse is GitHub for Biomedical Data
“Synapse is a compute platform for transparent,
reproducible, and modular collaborative research.”
• Data and code versioned • Every code change versioned
• Analysis history captured in real time • Every issue tracked
• Work anywhere, and share the results with anyone • Every project the starting point for new work
• Social/Interactive Science • Social/Interactive Coding
17. BRIDGE Seed Projects
Fanconi Diabetes
Melanoma
Anemia Activated
Hunt Community
Project
Breast Cancer Real Names
Genomic Parkinson’s
Research Project
19. BREAST CANCER GENOMIC RESEARCH: CURRENT APPROACHES
1. Isloated
breast cancer
cohorts
2. Many funders,
many disparate
Funded researchers 3. Data objectives
4. Clinical/genomic is siloed
data are accessible
but minimally
useable
5. Little incentive to
annotate data and curate
for other scientists
6. Limited impact of 7. Many published
today’s fragmented breast cancer
data on standard-of- prognosis models
care improvements but little consensus
19
for breast cancer
21. BREAST CANCER PROGNOSIS “CO-OPETITIONS” TO BUILD BETTER
DISEASE MODELS TOGETHER
2. Core/surgical
biopsy
Path lab Novel Data usage
Clinical
informatics
1. Activated 8. Field-test best models
breast cancer in clinic and hospital
patients
3. Aggregate 7. Give back education
Com and risk assessment to
Findin
BC patient 5. Open community-
citizens
data via muni
Citizen based “co-opetitions”
gs
BRIDGE portal Portal forge new computational
ty models
Foru 6. “Co-opetitions”
leaderboard allows
4. BC data curated,
ms researchers to work
open and supported by together
analysis tools
21
22. Crowdsourced Research in Action
Sage Bionetworks- DREAM Breast Cancer Prognosis Challenge | The Dream Project 26/ 11/ 2012 11:39
Home Challenges Team Ranking Conferences Discussion Literature Reverse Engineering News Contact us Login / Register.
DREAM is a Dialogue for Reverse Engineering Assessments and Methods. The main
objective is to catalyze the interaction between experiment and theory in the area of cellular
network inference and quantitative model building in systems biology. A Model Challenge 26/ 11/ 2012 11:40
Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge
Click here to get started with the Sage Bionetworks - DREAM Breast Cancer Prognosis Challenge
NEW: Final phase of the challenge has started!
Science Translational Medicine Enter Search Term ADVANCED
AAAS.ORG FEEDBACK HELP LIBRARIANS
Announcement
1. To remind you, we have set a deadline of October 15 to receive all of your submitted models for scoring and for determining Challenge winners (using the METABRIC
data and then a little later this fall, using the Oslo-Val data). To make sure that none of you misses this crucial deadline, we will receive your models up to 11:59 pm
Pacific on October 15. Please don't miss this deadline!!
Sci TM Home Current Issue Rapid Publication Issue Archive Multimedia Sci TM Collections My Sci TM About Sci TM
2. To select the top model as assessed using METABRIC data, we will choose no more than 5 models from each individual or team.>Shortly Journals > Science Translational Medicine Hom e > 12 Septem ber 2012 >
Home Science
after the October 15 LaMarco, 4:(151): 151ec162
deadline, we will send out a message letting you know that unless we receive a note from you to the alternative, we will submit your 5 top-scoring models for the final
METABRIC model assessment (as listed on the October 15 leader board). Science Translational Medicine Prev | Table of Contents | Next
stm .sciencemag.org
Sci Transl Med 12 Septem ber 2012:
3. Please note that a key aspect of our judging procedure will be to confirm that your model code is readable and reusable (i.e., such that others could use it or combine it151ec162
Vol. 4, Issue 151, p.
Sci. Transl. Med. DOI: 10.1126/ scitranslmed.3004863
with their own code to build a new and potentially better model).
EDITORS' CHOICE
46 teams (or individuals)
Synopsis
COMPUTATIONAL BIOLOGY
A Model Challenge
The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical
>1700 models submitted
information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles. Kelly LaMarco
+ Author Affiliations
Background
Many outperformed clinical co-variance
What’ s first on the list in Robert Fulghum’ s book, All I Really Need to Know I Learned in Kindergarten?
Molecular diagnostics for cancer therapeutic decision-making are among the most promising applications of genomic technology. Several diagnostic tests have gained Second? “Play f air.” Designers of the open- science Sage/ DREAM Breast Cancer
“Share everything.”
regulatory approval in recent years. Molecular profiles have proved particularly powerful in adding prognosis information to standard clinical practice in breast cancer,
Prognosis Challenge learned these lessons well, and there is still tim e for other com putational
m odelers to join in the show- and- tell. This open com putational challenge to identify predictors of
predictions
using gene-expression-based diagnostic tests such as MammaPrint [1] and Oncotype Dx [2].
breast cancer progression is accepting subm issions of m odels until 15 October 2012.
Based on initial promising clinical results, computational approaches to infer molecular predictors of cancer clinical phenotypes are one of the most active areas of
Breast cancer is the second leading cause of cancer death am ong wom en in the United States. Despite
research in both industrial and academic institutions, leading to a flood of published reports of signatures predictive of cancer phenotypes. Several trends have that billions of dollars are spent each year on research and treatm ent, biom edical scientists
the fact emerged
through these numerous studies: 1) genes defining predictive signatures of the same phenotype often do not overlap across multiple studies; 2) predictive signaturesplete understanding of prognosis and survival rates, which vary greatly am ong patients.
have an incom
reported by one group may not prove robust in other studies; 3) there is no consensus regarding the most accurate signatures or computational methods for inferring Challenge is to use crowdsourcing to m old a com putational m odel that accurately
The goal of the
predicts breast cancer survival. Challenge participants are invited to use genom ic and clinical
predictive signatures; 4) there is no consensus regarding the added value of incorporating molecular data in addition to or instead of traditionally used clinical covariates.
24. MELANOMA Screening – Could it be better?
Education is derived Best accuracy of
from top-down clinical diagnosis =
experiential 64%
knowledge (Grin, 1990)
160k new cases/year
48k deaths in 2012
in US HPI
ABCDE Both intra- and
“ugly duckling” inter- institutional
MD Dermoscopy
Pathology
data are siloed
Molecular
?Photos
There is no standard
screening program for
skin lesions; seeing an
MD is self directed
24
26. Initial focus on building the data needed
Novel Data collection 4. Give back risk-
+ Usage assessment & education
to the citizens
1.Activated citizens
take skin pictures
virtual cycle:
continuous
2. Store
tons of data!
aggregation of data
enriching the model
3. Run
algorithmic
cChallenges in
the compute
26
space
27. Data handling and governance
Data collection and storage Participant Consent
Genetic and other test
results
Electronic medical records
Journals – history and
progressions
Structured Surveys
Self-generated images
28. Next steps to Distributed Decoding of Diseases
Make the
benefit
Borrowing apparent
Finding
Adjacent
Next Gen
Reward
Foundations Shifting from
Structures
Finite to
Infinite
Finding Challenges
Activated
Communities BRIDGE
29. Sage Bionetworks:
BRIDGE
(are you making the right investments?)
How are you activating citizens?
How are you shifting rewards and
incentives?
Stephen H Friend
President Sage Bionetworks
(Non-Profit Foundation)