This document discusses building models of disease using data intensive science. It describes integrating omics data and computational models in a compute space. The challenges of the current drug discovery process are outlined, noting a need to better understand disease biology before testing compounds. Network models are proposed to capture disease complexity beyond single components. Examples are given of building gene co-expression networks from large datasets and using them to identify disease modules and key drivers. The potential for predictive models of genotype-specific drug responses is also mentioned.
Virscidian Poster Asms2010 Final Version LetterMark Bayliss
ASMS 2010 Poster - Mark Bayliss, Virscidian Inc - Towards automated evaluation of result accuracy for LC/MS/UV/ELSD/CLND substance screening – supporting Library Management and Medicinal Chemistry
MICCAI - Workshop on High Performance and Distributed Computing for Medical I...Joel Saltz
Keynote talk at HP-MICCAI / MICCAI-DCI 2011, The Joint Workshop on High Performance and Distributed Computing for Medical Imaging, featured at MICCAI 2011, held on September 22nd 2011 in Toronto, Canada
Protein-Protein Interaction using SVM based kernel,Jacob Coefficient and Gene...Ronak Shah
Protein-Protein interactions discovered by the existing high-throughput techniques contain very high amount of false positives. Here we present an SVM based approach to generate a model that is built on sequence and non-sequence based information of the interacting proteins. This model is used to assess the reliability of given protein-protein interactions. It was run on the interaction data of a pathogenic bacterium; Treponema pallidum (causes Syphilis in humans) obtained from Yeast two hybrid experiments. Various kernels were used for building the model and of all, Sigmoid kernel performed well when used with all the features combined with area under the receiver operating curve (ROC) as 0.53.
Model averaging in dose-response study in microarray expressionSetia Pramana
Dose-response studies recently have been integrated with microarray technologies. Within this setting, the response is gene-expression measured at a certain dose level. In this study, genes which are not differentially expressed are filtered out using a monotonic trend test. Then for the genes with significant monotone trend, several dose-response models were fitted. Afterward model averaging technique is carried for estimating the of target dose, ED50.
Presented in All models are wrong...
Model uncertainty & selection in complex models workshop, Groningen 14-16 march 2011
Virscidian Poster Asms2010 Final Version LetterMark Bayliss
ASMS 2010 Poster - Mark Bayliss, Virscidian Inc - Towards automated evaluation of result accuracy for LC/MS/UV/ELSD/CLND substance screening – supporting Library Management and Medicinal Chemistry
MICCAI - Workshop on High Performance and Distributed Computing for Medical I...Joel Saltz
Keynote talk at HP-MICCAI / MICCAI-DCI 2011, The Joint Workshop on High Performance and Distributed Computing for Medical Imaging, featured at MICCAI 2011, held on September 22nd 2011 in Toronto, Canada
Protein-Protein Interaction using SVM based kernel,Jacob Coefficient and Gene...Ronak Shah
Protein-Protein interactions discovered by the existing high-throughput techniques contain very high amount of false positives. Here we present an SVM based approach to generate a model that is built on sequence and non-sequence based information of the interacting proteins. This model is used to assess the reliability of given protein-protein interactions. It was run on the interaction data of a pathogenic bacterium; Treponema pallidum (causes Syphilis in humans) obtained from Yeast two hybrid experiments. Various kernels were used for building the model and of all, Sigmoid kernel performed well when used with all the features combined with area under the receiver operating curve (ROC) as 0.53.
Model averaging in dose-response study in microarray expressionSetia Pramana
Dose-response studies recently have been integrated with microarray technologies. Within this setting, the response is gene-expression measured at a certain dose level. In this study, genes which are not differentially expressed are filtered out using a monotonic trend test. Then for the genes with significant monotone trend, several dose-response models were fitted. Afterward model averaging technique is carried for estimating the of target dose, ED50.
Presented in All models are wrong...
Model uncertainty & selection in complex models workshop, Groningen 14-16 march 2011
*Watch the video at the end of the presentation
Seminar led by Dr. Xavier de la Cruz, ICREA Research Professor. Head of the Translational Bioinformatics in Neuroscience group of VHIR, at VHIR (22nd November 2012).
Content: The need to identify the pathological character of mutations may arise in different contexts in biomedical research. However, the methods available to address this problem essentially depend on the number of cases under analysis. When we work with only a few mutations we can use an artisan-like approach, where all information available on protein sequence, structure and function is manually retrieved and studied. However, when we need to characterize many variants, as can be the case in exome projects, faster methods are required to assess their pathogenicity. In my talk I will illustrate the principles underlying these two approaches with examples from the study of Fabry disease mutations, resulting from our collaborative work at the VHIR.
Biotechnology Industry has changed a lot during last decade , which means moving ahead from traditional ways to more advanced technological developments
2012 Biotechnology Industry is not the same as it was in 2001
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
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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.
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.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Stephen Friend Institute for Cancer Research 2011-11-01
1. Actionable Cancer Network Models
And Open Medical Information Systems
Integrating layers of omics data models and compute spaces
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam
ICR Oslo
November 1, 2011
2.
3.
4.
5. Why not use data intensive science
to build models of disease
Current Reward Structures
Organizational Structures and Tools
Pilots
Opportunities
6. What is the problem?
• Regulatory hurdles too high?
• Low hanging fruit picked?
• Payers unwilling to pay?
• Genome has not delivered?
• Valley of death?
• Companies not large enough to execute on strategy?
• Internal research costs too high?
• Clinical trials in developed countries too expensive?
In fact, all are true but none is the real problem
7. What
is
the
problem?
We
need
to
rebuild
the
drug
discovery
process
so
that
we
be6er
understand
disease
biology
before
tes8ng
proprietary
compounds
on
sick
pa8ents
8. What
is
the
problem?
Most
approved
cancer
therapies
assumed
tumor
indica8ons
would
represent
homogenous
popula8ons
Most
new
cancer
therapies
are
in
search
of
single
altered
components
Our
exis8ng
tumor
models
o>en
assume
pathway
knowledge
sufficinet
to
infer
correct
therapies
13. “Data Intensive” Science- Fourth Scientific Paradigm
Equipment capable of generating
massive amounts of data
IT Interoperability
Open Information System
Host evolving computational models
in a “Compute Space”
14.
15.
16.
17. WHY
NOT
USE
“DATA
INTENSIVE”
SCIENCE
TO
BUILD
BETTER
DISEASE
MAPS?
18. what will it take to understand disease?
DNA
RNA
PROTEIN
(dark
maGer)
MOVING
BEYOND
ALTERED
COMPONENT
LISTS
20. How is genomic data used to understand biology?
RNA amplification
Tumors
Microarray hybirdization
Tumors
Gene Index
Standard GWAS Approaches Profiling Approaches
Identifies Causative DNA Variation but Genome scale profiling provide correlates of disease
provides NO mechanism Many examples BUT what is cause and effect?
Provide unbiased view of
molecular physiology as it
relates to disease phenotypes
trait
Insights on mechanism
Provide causal relationships
and allows predictions
20
Integrated Genetics Approaches
21. Integration of Genotypic, Gene Expression & Trait Data
Schadt et al. Nature Genetics 37: 710 (2005)
Millstein et al. BMC Genetics 10: 23 (2009)
Causal Inference
“Global Coherent Datasets”
• population based
• 100s-1000s individuals
Chen et al. Nature 452:429 (2008) Zhu et al. Cytogenet Genome Res. 105:363 (2004)
Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)
22. Gene Co-Expression Network Analysis
Define a Gene Co-expression Similarity
Define a Family of Adjacency Functions
Determine the AF Parameters
Define a Measure of Node Distance
Identify Network Modules (Clustering)
Relate the Network Concepts to
External Gene or Sample Information 22
Zhang B, Horvath S. Stat Appl Genet Mol Biol 2005
23. Constructing Co-expression Networks
Start with expression measures for genes most variant genes across 100s ++ samples
1 2 3 4 Note: NOT a gene
expression heatmap
1
1 0.8 0.2 -0.8
Establish a 2D correlation matrix 2
for all gene pairs
expression
0.8 1 0.1 -0.6
3
0.2 0.1 1 -0.1
4
-0.8 -0.6 -0.1 1
Brain sample
Correlation Matrix
Define Threshold
eg >0.6 for edge
1 2 4 3 1 2 3 4
1 1
1 4 1 1 1 0 1 1 0 1
2 2
1 1 1 0 1 1 0 1
1 1 1 0 Hierarchically 3
Identify modules 4 0 0 1 0
2 3 cluster
4
3 0 0 0 1 1 1 0 1
Network Module Clustered Connection Matrix Connection Matrix
sets of genes for which many
pairs interact (relative to the
total number of pairs in that
set)
24. Preliminary Probabalistic Models- Rosetta /Schadt
Networks facilitate direct
identification of genes that are
causal for disease
Evolutionarily tolerated weak spots
Gene symbol Gene name Variance of OFPM Mouse Source
explained by gene model
expression*
Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics
Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics
Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg
Mirochnitchenko (University of
Medicine and Dentistry at New
Jersey, NJ) [12]
Lactb Lactamase beta 52% tg Constructed using BAC transgenics
Me1 Malic enzyme 1 52% ko Naturally occurring KO
Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple
(UCLA) [13]
Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg
(Columbia University, NY) [11]
C3ar1 Complement component 46% ko Purchased from Deltagen, CA
3a receptor 1
Tgfbr2 Transforming growth 39% ko Purchased from Deltagen, CA
Nat Genet (2005) 205:370 factor beta receptor 2
25. Our ability to integrate compound data into our network analyses
db/db mouse
(p~10E(-30))
= up regulated
= down regulated
db/db mouse
(p~10E(-20)
p~10E(-100))
AVANDIA in db/db mouse
26. Genomic
Literature
Protein-‐Protein
Complexes
Transcriptiona
l
Signaling
THE EVOLUTION OF SYSTEMS BIOLOGY
Mol.
Profiles
Structure
Model
Evolution
Disease
Models
Model
Topology
Physiologic
/
Model
Dynamics
Pathologic
Phenotype
Regulation
27. Extensive Publications now Substantiating Scientific Approach
Probabilistic Causal Bionetwork Models
• >80 Publications from Rosetta Genetics
Metabolic "Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003)
Disease "Variations in DNA elucidate molecular networks that cause disease." Nature. (2008)
"Genetics of gene expression and its effect on disease." Nature. (2008)
"Validation of candidate causal genes for obesity that affect..." Nat Genet. (2009)
….. Plus 10 additional papers in Genome Research, PLoS Genetics, PLoS Comp.Biology, etc
CVD "Identification of pathways for atherosclerosis." Circ Res. (2007)
"Mapping the genetic architecture of gene expression in human liver." PLoS Biol. (2008)
…… Plus 5 additional papers in Genome Res., Genomics, Mamm.Genome
Bone "Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005)
d
..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009)
Methods "An integrative genomics approach to infer causal associations ... Nat Genet. (2005)
"Increasing the power to detect causal associations… PLoS Comput Biol. (2007)
"Integrating large-scale functional genomic data ..." Nat Genet. (2008)
…… Plus 3 additional papers in PLoS Genet., BMC Genet.
28. List of Influential Papers in Network Modeling
50 network papers
http://sagebase.org/research/resources.php
30. “Data Intensive” Science- Fourth Scientific Paradigm
Score Card for Medical Sciences
Equipment capable of generating
massive amounts of data A-
IT Interoperability D
Open Information System D-
Host evolving computational models
in a “Compute Space F
31. We still consider much clinical research as if we we
hunter gathers - not sharing
.
38. sharing as an adoption of common standards..
Clinical Genomics Privacy IP
39. Sage Mission
Sage Bionetworks is a non-profit organization with a vision to
create a commons where integrative bionetworks are evolved by
contributor scientists with a shared vision to accelerate the
elimination of human disease
Building Disease Maps Data Repository
Commons Pilots Discovery Platform
Sagebase.org
41. NEW MAPS
Disease Map and Tool Users-
( Scientists, Industry, Foundations, Regulators...)
PLATFORM
Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)
PILOTS= PROJECTS FOR COMMONS
Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....)
NEW TOOLS
ORM
APS
Data Tool and Disease Map Generators-
(Global coherent data sets, Cytoscape,
M
F
PLAT
Clinical Trialists, Industrial Trialists, CROs…)
NEW
RULES GOVERN RULES AND GOVERNANCE
Data Sharing Barrier Breakers-
(Patients Advocates, Governance
and Policy Makers, Funders...)
42. Bin Zhang
Integration of Multiple Networks for Jun Zhu
Pathway and Target Identification
CNV
Data
Gene
Expression
Clinical
Traits
Co-Expression
Bayesian Network
Network
Integration of Coexp. &
Bayesian Networks 42
Key Driver Analysis
42
43. Bin Zhang
Key Driver Analysis Jun Zhu
Justin Guinney
http://sagebase.org/research/tools.php 43
44. Bin Zhang
Model of Breast Cancer: Co-expression Xudong Dai
Jun Zhu
A) Miller 159 samples B) Christos 189 samples
NKI: N Engl J Med. 2002 Dec 19;347(25):1999.
Wang: Lancet. 2005 Feb 19-25;365(9460):671.
Miller: Breast Cancer Res. 2005;7(6):R953.
Christos: J Natl Cancer Inst. 2006 15;98(4):262.
C) NKI 295 samples
E) Super modules
Cell cycle
Pre-mRNA
ECM
D) Wang 286 samples Blood vessel
Immune
response
44
Zhang B et al., Towards a global picture of breast cancer (manuscript).
45. Bin Zhang
Model of Breast Cancer: Integration Xudong Dai
Jun Zhu
Conserved Super-modules
mRNA proc.
= predictive
Breast Cancer Bayesian Network
Chromatin
of survival
Extract gene:gene relationships for selected super-modules from BN and define Key Drivers
Pathways & Regulators
(Key drivers=yellow; key drivers validated in siRNA screen=green)
Cell Cycle (Blue) Chromatin Modification (Black) Pre-mRNA proc. (Brown) mRNA proc. (red)
45
Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)
47. Developing predictive models of genotype-specific
sensitivity to compound treatment
Gene8c
Feature
Matrix
Expression,
copy
number,
somaQc
mutaQons,
etc.
Predic8ve
Features
(biomarkers)
Cancer
samples
with
varying
degrees
of
response
to
therapy
Sensi8ve
Refractory
(e.g.
EC50)
47
48. 1
20
100
500
Feature
Features
Features
Features
Elastic net regression
48
54. Why not share clinical /genomic data and model building in the
ways currently used by the software industry
(power of tracking workflows and versioning
57. sage bionetworks synapse project
Watch What I Do, Not What I Say Reduce, Reuse, Recycle
My Other Computer is Amazon
Most of the People You Need to Work with
Don’t Work with You
58. Six
Pilots
at
Sage
Bionetworks
CTCAP
Non-‐Responders
Arch2POCM
ORM
S
MAP
The
FederaQon
F
PLAT
Portable
Legal
Consent
NEW
Sage
Congress
Project
RULES GOVERN
59. CTCAP
Clinical Trial Comparator Arm Partnership “CTCAP”
Strategic Opportunities For Regulatory Science
Leadership and Action
FDA
September 27, 2011
60. Clinical Trial Comparator Arm
Partnership (CTCAP)
Description: Collate, Annotate, Curate and Host Clinical Trial Data
with Genomic Information from the Comparator Arms of Industry and
Foundation Sponsored Clinical Trials: Building a Site for Sharing
Data and Models to evolve better Disease Maps.
Public-Private Partnership of leading pharmaceutical companies,
clinical trial groups and researchers.
Neutral Conveners: Sage Bionetworks and Genetic Alliance
[nonprofits].
Initiative to share existing trial data (molecular and clinical) from
non-proprietary comparator and placebo arms to create powerful
new tool for drug development.
Started Sept 2010
61. Shared clinical/genomic data sharing and analysis will
maximize clinical impact and enable discovery
• Graphic
of
curated
to
qced
to
models
62. Non-‐Responders
Project
To identify Non-Responders to approved
Oncology drug regimens in order to improve
outcomes, spare patients unnecessary toxicities
from treatments that have no benefit to them, and
reduce healthcare costs
63. The
Non-‐Responder
Cancer
Project
Leadership
Team
Stephen Friend, MD, PhD
Todd Golub, MD
Founding Director Cancer Biology
President and Co-Founder of
Program Broad Institute, Charles Dana
Sage Bionetworks, Head of
Investigator Dana-Farber Cancer
Merck Oncology 01-08,
Institute, Professor of Pediatrics Harvard
Founder of Rosetta
Medical School, Investigator, Howard
Inpharmatics 97-01, co-
Hughes Medical Institute
Founder of the Seattle Project
Richard Schilsky, MD
Garry Nolan, PhD Chief, Hematology- Oncology, Deputy
Professor, Baxter Laboratory of Stem Director, Comprehensive Cancer
Cell Biology, Department of Microbiology Center, University of Chicago; Chair,
and Immunology, Stanford University National Cancer Institute Board of
Director, Proteomics Center at Stanford Scientific Advisors; past-President
University
ASCO, past Chairman CALGB clinical
trials group
11
64. The
Non-‐Responder
Project
is
an
internaQonal
iniQaQve
with
funding
for
6
iniQal
cancers
anQcipated
from
both
the
public
and
private
sectors
GEOGRAPHY
United
States
China
TARGET
CANCER
Ovarian
Renal
Breast
AML
Colon
Lung
RetrospecQve
FUNDING
SOURCE
Seeking
private
sector
and
study;
likely
to
Funded
by
the
Chinese
government
philanthropic
funding
for
be
funded
by
and
private
sector
partners
the
Federal
prospec8ve
studies
Government
5
65. Arch2POCM
Restructuring
the
PrecompeQQve
Space
for
Drug
Discovery
How
to
potenQally
De-‐Risk
High-‐Risk
TherapeuQc
Areas
68. How can we accelerate the pace of scientific discovery?
2008
2009
2010
2011
Ways to move beyond
“traditional” collaborations?
Intra-lab vs Inter-lab
Communication
Colrain/ Industrial PPPs Academic
Unions
71. sage federation:
model of biological age
Faster Aging
Predicted
Age
(liver
expression)
Slower Aging
Clinical Association
- Gender
- BMI
- Disease
Age Differential Genotype Association
Gene Pathway Expression
Chronological
Age
(years)
72. Reproducible
science==shareable
science
Sweave: combines programmatic analysis with narrative
Dynamic generation of statistical reports
using literate data analysis
Sweave.Friedrich Leisch. Sweave: Dynamic generation of statistical reports
using literate data analysis. In Wolfgang Härdle and Bernd Rönz,editors, Compstat 2002 –
Proceedings in Computational Statistics,pages 575-580.
Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9
73. Federated
Aging
Project
:
Combining
analysis
+
narraQve
=Sweave Vignette
Sage Lab
R code + PDF(plots + text + code snippets)
narrative
HTML
Data objects
Califano Lab Ideker Lab Submitted
Paper
Shared
Data
JIRA:
Source
code
repository
&
wiki
Repository
79. Why not use data intensive science
to build models of disease
Current Reward Structures
Organizational Structures and Tools
Six Pilots
Opportunities