In Search of Better Tools and Capabilities
"We need to do more with less." This is a common theme we hear in our conversations and follow up with Researchers in early phase drug discovery.
To us this translates into better identification and more intense screening of potential therapeutic compounds and targets. Failure is success if it is determined before animal testing.
The foundation being potent, pure and easy to culture primary cells. Then building on the platform with the required media/growth factors, markers, transfection reagents and apoptosis detection kits.
Using methylation patterns to determine origin of biological material and ageQIAGEN
In this QIAGEN sponsored webinar, our guest speakers from the San Francisco Police Department (SFPD) Crime Lab and Florida International University (FIU) discuss their research on the potential of epigenetic methylation as a procedure for body fluid identification and age estimation from DNA left at crime scenes. Several approaches have been studied, including an analysis of methyl array data and an initial validation of procedures such as pyrosequencing and real-time PCR. The presentation focuses on a number of tissue-specific epigenetic markers for body fluid and age determination with a promise of future integration of these markers into the forensic lab due to the simplicity of analysis and the ease of application.
Learn more about the Pyrosequencing technology and our solutions at
https://www.qiagen.com/resources/technologies/pyrosequencing-resource-center/
DNA microarray:
A DNA microarray (also commonly known as gene or genome chip, DNA chip, or gene array) is a collection of microscopic DNA spots, commonly representing single genes, arrayed on a solid surface by covalent attachment to a chemical matrix. DNA arrays are different from other types of microarray only in that they either measure DNA or use DNA as part of its detection system. Qualitative or quantitative measurements with DNA microarrays utilize the selective nature of DNA-DNA or DNA-RNA hybridization under high-stringency conditions and fluorophore-based detection. DNA arrays are commonly used for expression profiling, i.e., monitoring expression levels of thousands of genes simultaneously.
Cell based assays presentation v3_03_2012Pete Shuster
Update presentation on "increasing returns in drug discovery by harnessing the power of cells". Includes images/data/pubs of differentiating human sensory and dopaminergic neurons from hNP1 neural progenitors + osteoblasts and chondrocytes from human mesenchymal stem cells. Our platforms are ideal for high throughput screening and other drug discovery processes.
Bert Reijmerink (Genalice) - Hoe technologie bijdraagt aan een betere behande...AlmereDataCapital
Presentatie van Bert Reijmerink (Genalice) - 'Hoe technologie bijdraagt aan een betere behandeling van kankerpatiënten' tijdens het Big Data Analytics seminar 14 juni in Almere
In Search of Better Tools and Capabilities
"We need to do more with less." This is a common theme we hear in our conversations and follow up with Researchers in early phase drug discovery.
To us this translates into better identification and more intense screening of potential therapeutic compounds and targets. Failure is success if it is determined before animal testing.
The foundation being potent, pure and easy to culture primary cells. Then building on the platform with the required media/growth factors, markers, transfection reagents and apoptosis detection kits.
Using methylation patterns to determine origin of biological material and ageQIAGEN
In this QIAGEN sponsored webinar, our guest speakers from the San Francisco Police Department (SFPD) Crime Lab and Florida International University (FIU) discuss their research on the potential of epigenetic methylation as a procedure for body fluid identification and age estimation from DNA left at crime scenes. Several approaches have been studied, including an analysis of methyl array data and an initial validation of procedures such as pyrosequencing and real-time PCR. The presentation focuses on a number of tissue-specific epigenetic markers for body fluid and age determination with a promise of future integration of these markers into the forensic lab due to the simplicity of analysis and the ease of application.
Learn more about the Pyrosequencing technology and our solutions at
https://www.qiagen.com/resources/technologies/pyrosequencing-resource-center/
DNA microarray:
A DNA microarray (also commonly known as gene or genome chip, DNA chip, or gene array) is a collection of microscopic DNA spots, commonly representing single genes, arrayed on a solid surface by covalent attachment to a chemical matrix. DNA arrays are different from other types of microarray only in that they either measure DNA or use DNA as part of its detection system. Qualitative or quantitative measurements with DNA microarrays utilize the selective nature of DNA-DNA or DNA-RNA hybridization under high-stringency conditions and fluorophore-based detection. DNA arrays are commonly used for expression profiling, i.e., monitoring expression levels of thousands of genes simultaneously.
Cell based assays presentation v3_03_2012Pete Shuster
Update presentation on "increasing returns in drug discovery by harnessing the power of cells". Includes images/data/pubs of differentiating human sensory and dopaminergic neurons from hNP1 neural progenitors + osteoblasts and chondrocytes from human mesenchymal stem cells. Our platforms are ideal for high throughput screening and other drug discovery processes.
Bert Reijmerink (Genalice) - Hoe technologie bijdraagt aan een betere behande...AlmereDataCapital
Presentatie van Bert Reijmerink (Genalice) - 'Hoe technologie bijdraagt aan een betere behandeling van kankerpatiënten' tijdens het Big Data Analytics seminar 14 juni in Almere
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
2015 Computational genomics course poster. There will be theoretical lectures followed by practical session where students apply what they learned. The programming will be mainly done in R. The course will be beneficial for first year computational biology PhD students or experimental biologists who want to start data analysis or seeking a better understanding of computational genomics.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
An Introduction to Bioinformatics
Drexel University INFO648-900-200915
A Presentation of Health Informatics Group 5
Cecilia Vernes
Joel Abueg
Kadodjomon Yeo
Sharon McDowell Hall
Terrence Hughes
Prof. Mark Coles (Oxford University) - Data-driven systems medicinemntbs1
The summary of Prof. Mark Coles' presentation from the Jun 11-12th 2019 event Data-driven systems medicine at Cardiff University Brain Research Imaging Centre.
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
“I think the biggest innovations of the 21st century will be at the intersection of biology and technology. A new era is beginning.” — Steve Jobs
While analyzing the effects of radio frequency heating on hypothermia in the year 1941, Canadian electrical engineer John Hopps read that if the heart stops beating due to an acute drop in temperature, it could successfully be brought back to life artificially using mechanical or electrical stimulation.
BioNetVisA 2018 ECCB workshop
From biological network reconstruction to data visualization and analysis in molecular biology and medicine.
http://eccb18.org/workshop-2/
https://bionetvisa.github.io/
PROKARYOTIC TRANSCRIPTOMICS AND METAGENOMICSLubna MRL
After billions of years of evolution, prokaryotes have developed a huge diversity of regulatory mechanisms, many of which are probably uncharacterized. Now that the powerful tool of whole-transcriptome analysis can be used to study the RNA of bacteria and archaea, a new set of un expected RNA-based regulatory strategies might be revealed.
Metagenomics, together with in vitro evolution and high-throughput screening technologies, provides industry with an unprecedented chance to bring biomolecules into industrial application.
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.
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!
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.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
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
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.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...
Stephen Friend Complex Traits: Genomics and Computational Approaches 2012-02-23
1. If the physicists do it, the software engineers do it,
Why can’t we do it?:
Moving beyond linear investigations
Both of the science and of how we work
Integrating layers of omics data models and building
using compute spaces capable of enabling models
to be evolved by teams of teams
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam
February 23, 2012
2.
3. So
what
is
the
problem?
Most
approved
therapies
were
assumed
to
be
monotherapies
for
diseases
represen4ng
homogenous
popula4ons
Our
exis4ng
disease
models
o9en
assume
pathway
knowledge
sufficient
to
infer
correct
therapies
8. “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”
9.
10.
11. WHY
NOT
USE
“DATA
INTENSIVE”
SCIENCE
TO
BUILD
BETTER
DISEASE
MAPS?
12. what will it take to understand disease?
DNA
RNA
PROTEIN
(dark
maHer)
MOVING
BEYOND
ALTERED
COMPONENT
LISTS
14. 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
18. “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
19. We still consider much clinical research as if we were
hunter gathers - not sharing
.
26. 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
28. JUN ZHU
Model of Breast Cancer: Co-expression
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
28
Zhang B et al., Towards a global picture of breast cancer (manuscript).
29. CHRIS
GAITERI-‐ALZHEIMER’S
What
is
this?
Bayesian
networks
enriched
in
inflammaQon
genes
correlated
with
disease
severity
in
pre-‐frontal
cortex
of
250
Alzheimer’s
paQents.
What
does
it
mean?
InflammaQon
in
AD
is
an
interacQve
mulQ-‐pathway
system.
More
broadly,
network
structure
organizes
complex
disease
effects
into
coherent
sub-‐systems
and
can
prioriQze
key
genes.
Are
you
joking?
Gene
validaQon
shows
novel
key
drivers
increase
Abeta
uptake
and
decrease
neurite
length
through
an
ROS
burst.
(highly
relevant
to
AD
pathology)
30. ELIAS NETO Causal Model Selection Hypothesis Tests in Systems Genetics
Elias Chaibub Neto1, Aimee T. Broman2, Mark P. Keller2, Alan D. Attie2, Bin Zhang1, Jun Zhu1, Brian S. Yandell2
1 Sage Bionetworks, Seattle, WA USA; 2 University of Wisconsin-Madison, Madison, WI USA
Abstract Vuong’s Model Selection Test Causal Model Selection Tests (CMST) Simulation Study
Current efforts in systems genetics have focused on the Vuong's test derives from the Kullback-Leibler Information In our applications we consider four models: M1, M2, M3 and We conducted a simulation study generating data from the
development of statistical approaches aiming to disentangle Criterion (KLIC). M4. models on
causal relationships among molecular phenotypes in segregating the Figure below.
populations. Model selection criterions, such as the AIC and Let h0(y | x) represent the true model. We derive intersection-union tests based on six separate Vuong
BIC, have been widely used for this purpose, in spite of being (Clarke) tests:
unable to quantify the uncertainty associated with the model Consider the parametric family of conditional models: {f(y | x; f1 vs f2 , f1 vs f3 , f1 vs f4 , f2 vs f3 , f2 vs f4 , f3 vs f4
selection call. Here we propose three novel hypothesis tests to φ): φ ϵ Ф}.
perform model selection among models representing distinct We propose three distinct CMST tests: (1) parametric, (2) non-
Then parametric, and (3) joint-parametric CMST tests.
causal relationships. We focus on models composed of pairs of
phenotypes and use their common QTL to determine which KLIC(h0, f) = E0[log h0(y | x)] – E0[log f(y | x; φ)], The results are shown below:
phenotype has a causal effect on the other, or whether the
phenotypes are not causally related, and are only statistically where the expectation E0 is computed w.r.t h0(y, x), and φ* is the Parametric CMST:
associated. Our hypothesis tests are fully analytical and avoid parameter value that minimizes KLIC(h0, f).
H0: model M1 is not closer to the true model than M2, M3 or M4.
the use of computationally expensive permutation or re-sampling
Consider two models: f1 ≡ f1(y | x; φ1*) and f2 ≡ f2(y | x; φ2*). H1: model M1 is closer to the true model than M2, M3 and M4.
strategies. They adapt and extend Vuong's (and Clarke’s) model
selection test to the comparison of four possibly misspecified
models, handling the full range of possible causal relationships Model f1 is a better approximation of h0 than f2 if and only if H0: { E0[LR12] = 0 } { E0[LR13] = 0 } { E0[LR14] = 0 }
among a pair of phenotypes. We evaluate the performance of our H1: { E0[LR12] > 0 } ∩ { E0[LR13] > 0 } ∩ { E0[LR14] > 0 }
tests against the AIC, BIC and a published causality inference KLIC(h0, f1) < KLIC(h0, f2) E0[log f1] > E0[log f2].
The rejection region and p-value for this IU-test are given by:
test in simulation studies. Furthermore, we compare the
precision of the causal predictions made by the methods using Let LR12 = log f1 – log f2. Then we test
biologically validated causal relationships extracted from a min{z12 , z13 , z14} > cα , p1 = max{p12 , p13 , p14}.
database of 247 knockout experiments in yeast. Overall, our H0: E0[LR12] = 0, H1: E0[LR12] > 0, H2: E0[LR12] < 0.
model selection hypothesis tests achieve higher precision than
the alternative methods at the expense of reduced statistical The quantity E0[LR12] is unknown, but the sample mean and Non-parametric CMST:
power. variance of
Analogous to the parametric CMST. Just replace Vuong’s by
LR = log f – log f 2,i, f 1 ≡ f(y | x; φ 1), φ ≡
12,i 1,i 1 Clarke’s tests.
ML est. of φ1
Pairwise Causal Models
converve a.s. to E0[LR12] and Var0[LR12] = σ12.12 . Joint parametric CMST:
Given a pair of phenotypes, Y1 and Y2, that co-map to the same
quantitative trait loci, Q, we consider the following models: Let LR = ∑ LR , then under H0
12 12,i Simple application of Vuong tests, overlooks the dependency
among the test statistics.
(n σ 12.12 )−1/2 LR 12 →d N(0, 1).
Let S1 represent the sample covariance matrix of LR 12,i , Yeast Data Analysis
If different models have different dimensions we consider
LR 13,i and LR 14,i.
We analyzed the yeast genetical genonics data set from Brem
LR *12 = LR 12 – D12 Under regularity conditions we have that S1 converges a.s. to and Kruglyak (2005).
Σ1.
where D12 represents a difference of AIC or BIC penalties, and We evaluated the precision of the causal predictions made by
adopt the test statistic the methods using validated causal relationships extracted
It follows from the MCT and Slutsky’s theorem that when
Z12 = (n σ 12.12 )−1/2 LR *12 . from a data-base of 247 knock-out experiments (Hughes
( E0[LR12] , E0[LR13] , E0[LR14] )T = ( 0 , 0 , 0 )T 2000, Zhu 2008).
Clarke’s Model Selection Test
we have that In total, 46 of the ko-genes showed significant eQTLs, and
Conclusions Represents a non-parametric version of Vuong’s test. we tested a total of 4,928 ko-gene/putative target gene
Z1 = n−1/2 diag(S1 )−1/2 LR 1 →d N3(0 , ρ1) relations.
Advantages of the Causal Model Selection Tests: Vuong’s null: the mean log-likelihood ratio is 0.
Clarke’s null: the median log-likelihood ratio is 0. where LR 1 = ( LR 12 , LR 13 , LR 14 )T and ρ1 = diag
1- Fully analytical hypothesis tests that avoid the use of (S1)−1/2 Σ1 diag(S1)−1/2
computationally expensive permutation or re-sampling Paired sign test on log-likelihood scores:
techniques. We consider the hypotheses
Scores: (LR 12,1 , LR 12,2 , LR 12,3 , LR 12,4 , LR 12,5 ,
2- Achieve better controlled type I error rates. … , LR 12,n ) H0: min{ E0[LR12] , E0[LR13] , E0[LR14] } ≤ 0
Signs: ( + , − , + , + , − , … , H1: min{ E0[LR12] , E0[LR13] , E0[LR14] } > 0
3- Achieve higher precision rates. + )
and adopt the test statistic W1 = min{Z1}. The p-value is
Let, T12 = {# of positive signs}. Then under Clarke’s null computed as
Main disadvantage: lower statistical power.
T12 ~ Binomial(n, 1/2). P(W1 ≥ w1) = P(Z12 ≥ w1 , Z13 ≥ w1 , Z14 ≥ w1).
31. ELIAS NETO
Causal Model Selection Hypothesis Tests in Systems Genetics
The Schadt et al. (2005) approach was based on
a penalized likelihood model selection approach,
were we simply select the model with the best
score.
The proposed hypothesis test allows us to attach
a p-value to the selected model and, in this way,
allows the quantification of the uncertainty
associated with the model selection call.
The proposed tests are fully analytical and avoid
computationally expensive permutation and re-
sampling techniques.
32. ZHI
WANG
A
mulQ-‐Qssue
immune-‐driven
theory
of
weight
loss
Hypothalamus
Lep4n
signaling
FaDy
acids
Macrophage/
inflamma4on
Liver
Adipose
M1
macrophage
Phagocytosis-‐
Phagocytosis-‐
induced
lipolysis
induced
lipolysis
33. PLATFORM
Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)
PILOTS= PROJECTS FOR COMMONS
Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....)
ORM
M APS
F
PLAT
NEW
RULES GOVERN
34.
35. Why not share clinical /genomic data and model building in the
ways currently used by the software industry
(power of tracking workflows and versioning
41. Sage Metagenomics Project
Processed Data
(S3)
• > 10k genomic and expression standardized datasets indexed in SCR
• Error detection, normalization in mG
• Access raw or processed data via download or API in downstream analysis
• Building towards open, continuous community curation
42. Sage Metagenomics using Amazon Simple Workflow
Full case study at http://aws.amazon.com/swf/testimonials/swfsagebio/
43. Amazon SWF and Synapse
• Maintains state of analysis • Hosts raw and processed data for
• Tracks step execution further reuse in public or private
projects
• Logs workflow history
• Provides visibility into
• Dispatches work to Amazon or intermediate results and
remote worker nodes algorithmic details
• Efficiently match job size to • Allows programmatic access to
hardware data; integration with R
• Provides error handling and • Provides standard terminologies
recovery for annotations
• Search across data sets
44. Synapse Roadmap
• Data Repository
• Projects and security Synapse Platform Functionality
• R integration • Workflow templates
• Analysis provenance • Social networking
• Publishing figures • User-customized
• Search • Wiki & collaboration tools dashboards
• Controlled Vocabularies • Integrated management • R Studio integration
• Governance of restricted of cloud resources • Curation tool integration
data
Internal Alpha Public Beta Testing Synapse 1.0 Synapse 1.5 Future
Q1-2012 Q2-2012 Q3-2012 Q4-2012 Q1-2013 Q2-2013 Q3-2013 Q4-2013
• TCGA • Predictive modeling • TBD: Integrations with other
• METABRIC breast workflows visualization and analysis
cancer challenge • Automated processing of packages
common genomics platforms
• 40+ manually curated clinical studies
• 8000 + GEO / Array Express datasets
• Clinical, genomic, compound sensitivity
• Bioconductor and custom R analysis
Data / Analysis Capabilities
46. Now
accep4ng
submissions
Editor-‐in-‐Chief
Eric
Schadt
(USA)
Open
Network
Biology
is
an
open
access
journal
that
publishes
arQcles
relaQng
to
predicQve,
network-‐based
models
of
living
systems
linked
to
the
corresponding
coherent
data
sets
upon
which
the
models
are
based.
In
addiQon
to
arQcles
describing
these
large
data
sets,
the
journal
also
welcomes
submissions
of
original
research,
sobware
and
methods,
along
with
reviews
and
commentary,
relevant
to
the
emerging
field
of
network
biology.
Submit
your
manuscript
and
benefit
from:
•
High
visibility
for
arQcles
through
unrestricted
online
access
•
Free
arQcle
redistribuQon
under
a
CreaQve
Commons
aHribuQon
license
•
No
limits
on
arQcle
length,
addiQonal
files,
colour
figures
or
movies
•
Rapid,
immediate
open
access
publicaQon
on
acceptance
•
An
integrated
repository
for
network
model
data
and
code
www.opennetworkbiology.com
47. Five
Pilots
involving
Sage
Bionetworks
CTCAP
Arch2POCM
The
FederaQon
ORM
S
Portable
Legal
Consent
MAP
F
Sage
Congress
Project
PLAT
NEW
RULES GOVERN
48. 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
49. Shared clinical/genomic data sharing and analysis will
maximize clinical impact and enable discovery
• Graphic
of
curated
to
qced
to
models
50. Arch2POCM
Restructuring
the
PrecompeQQve
Space
for
Drug
Discovery
How
to
potenQally
De-‐Risk
High-‐Risk
TherapeuQc
Areas
51.
52. Arch2POCM: scale and scope
• Proposed Goal: Initiate 2 programs. One for Oncology/Epigenetics/
Immunology. One for Neuroscience/Schizophrenia/Autism. Both
programs will have 8 drug discovery projects (targets) - ramped up
over a period of 2 years
– It is envisioned that Arch2POCM’s funding partners will select targets
that are judged as slightly too risky to be pursued at the top of pharma’s
portfolio, but that have significant scientific potential that could benefit
from Arch2POCM’s crowdsourcing effort
• These will be executed over a period of 5 years making a total of 16
drug discovery projects
– Projected pipeline attrition by Year 5 (assuming 12 targets loaded in
early discovery)
• 30% will enter Phase 1
• 20% will deliver Ph 2 POCM data 52
54. 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
56. 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)
57. 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
58. 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
60. Presentation outline
1)
Predic4ng
drug
response
2)
Future
approaches:
3)
Standardized
from
cancer
cell
lines
network-‐based
predictors
workflows
for
data
and
mul4-‐task
learning
management,
Cancer
cell
line
versioning
and
encyclopedia
method
comparison
Molecular characterization
Network
/
pathway
(1,000 cell lines) prior
informa4on
Currently
mRNA
copy number
somatic mutations (36
cancer-related genes)
In progress
targeted exon sequencing Vaske,
et
al.
epigenetics
microRNA TCGA
/ICGC
lncRNA Transfer
Molecular characterization
learning
(50 tumor types)
phospho-tyrosine kinase
metabolites
Viability screens (500 cell genomics
lines, 24 compounds)
transcriptomics
Small molecule screen epigenetics
Predic4ve
Clinical data
model
Vaske,
et
al.
61. 1) Data
management
APIs
to
load
standaridzed
objects,
e.g.
R
ExpressionSets
(MaD
Furia):
ccleFeatureData
<-‐
getEnQty(ccleFeatureDataId)
ccleResponseData
<-‐
getEnQty(ccleResponseDataId)
2)
tAutomated,
standardized
workflows
for
cura4on
and
QC
of
large-‐scale
datasets
(-‐
getEnQty(tcgaFeatureDataId)
cgaFeatureData
< Brig
Mecham).
tcgaResponseData
<-‐
getEnQty(tcgaResponseDataId)
A. TCGA:
Automated
cloud-‐based
processing.
B. GEO
/
Array
Expression:
NormalizaQon
workflows,
curaQon
of
phenotype
using
standard
ontologies.
C. AddiQonal
studies
with
geneQc
and
phenotypic
data
in
Sage
repository
(e.g.
CCLE
and
Sanger
cell
line
datasets)
Observed Data!=! Systematic Variation! +! Random Variation!
=! +! +!
3) Pluggable
API
to
implement
predic4ve
modeling
algorithms.
Normalization: Remove the influence of
adjustment variables on data...!
A) Support
for
all
commonly
used
machine
learning
methods
4) Sta4s4cal
performance
assessment
ew
methods)
(for
automated
benchmarking
against
n across
models.
B) Pluggable
custom
=! ethods
as
R
classes
implemenQng
m
customTrain()
and
customPredict()
methods.
+!
custom
model
1
be
arbitrarily
complex
(e.g.
pathway
and
other
A) Can
custom
model
2
custom
model
N
priors)
5) Output
of
candidate
biomarkers
and
feature
B) Support
for
parallelizaQon
in
for
each
loops.
evalua4on
(e.g.
GSEA,
pathway
analysis)
custom
model
1
custom
model
2
custom
model
N
6)
Experimental
follow-‐up
on
top
predic4ons
(TBD)
E.g.
for
cell
lines:
medium
throughput
suppressor
/
enhancer
screens
of
drug
sensiQvity
for
knockdown
/
overexpression
of
predicted
biomarkers.
67. Sage
Congress
Project
April
20
2012
RealNames
Parkinson’s
Project
RevisiQng
Breast
Cancer
Prognosis
Fanconi’s
Anemia
(Responders
CompeQQons-‐
IBM-‐DREAM)