The document discusses using data intensive science to build better models of disease. It argues that the current pharmaceutical model is broken because it lacks sufficient understanding of disease biology. Most drug candidates fail because existing disease models oversimplify complex conditions. The author proposes using large datasets and computational modeling to map molecular pathways and construct causal models of diseases. This could provide a more mechanistic understanding of diseases and their heterogeneity to identify true drivers and better predict treatment responses. Pilots are needed to determine if this approach can modify diseases rather than just treat symptoms by moving beyond lists of altered genes and proteins to causal networks.
Modeling XCS in class imbalances: Population sizing and parameter settingskknsastry
This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially.
The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.
Substructrual surrogates for learning decomposable classification problems: i...kknsastry
This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.
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
Modeling XCS in class imbalances: Population sizing and parameter settingskknsastry
This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially.
The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.
Substructrual surrogates for learning decomposable classification problems: i...kknsastry
This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.
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
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 10...Joy Elly Tulung
Upper Echelons Theory - Hambrick & Mason, 1984
Various studies show that organizations are a reflection of its top managers (Finkelstein and Hambrick) 1996
Carpenter et al (2004) also repeats the composition of the TMT, in terms of diversity of the upper echelons theory due to the duties of internal and external management.
TOP MANAGEMENT TEAM COMPOSITION ON INDONESIAN BANKING CASE STUDY: BANKS IN 10...Joy Elly Tulung
Upper Echelons Theory - Hambrick & Mason, 1984
Various studies show that organizations are a reflection of its top managers (Finkelstein and Hambrick) 1996
Carpenter et al (2004) also repeats the composition of the TMT, in terms of diversity of the upper echelons theory due to the duties of internal and external management.
*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.
IMPORTANT: If you want to get a clear review of the Differences & Complementarities Between « Heuristic » and « Mathematical » approaches, we invite you to download our presentation given during the EPA (European Psychiatric Association) conference in 2011 that is now utilized in training programs.
Similar to Stephen Friend Norwegian Academy of Science and Letters 2011-11-02 (20)
CDSCO and Phamacovigilance {Regulatory body in India}NEHA GUPTA
The Central Drugs Standard Control Organization (CDSCO) is India's national regulatory body for pharmaceuticals and medical devices. Operating under the Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, the CDSCO is responsible for approving new drugs, conducting clinical trials, setting standards for drugs, controlling the quality of imported drugs, and coordinating the activities of State Drug Control Organizations by providing expert advice.
Pharmacovigilance, on the other hand, is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The primary aim of pharmacovigilance is to ensure the safety and efficacy of medicines, thereby protecting public health.
In India, pharmacovigilance activities are monitored by the Pharmacovigilance Programme of India (PvPI), which works closely with CDSCO to collect, analyze, and act upon data regarding adverse drug reactions (ADRs). Together, they play a critical role in ensuring that the benefits of drugs outweigh their risks, maintaining high standards of patient safety, and promoting the rational use of medicines.
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!
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
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
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
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
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
- 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
Title: Sense of Taste
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 structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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
How STIs Influence the Development of Pelvic Inflammatory Disease.pptx
Stephen Friend Norwegian Academy of Science and Letters 2011-11-02
1. Future of Genetics in Medicine
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam
The Norwegian Academy of Science and Letters, Oslo
November 2, 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. Alzheimers Diabetes
Treating Symptoms v.s. Modifying Diseases
Autism Cancer
Will it work for me?
7. The Current Pharma Model is Broken:
• In 2010, the pharmaceutical industry spent ~$100B for R&D
• Half of the 2010 R&D spend ($50B) covered pre-PH III activities
• Half of the pre-PH III costs ($25B) were for program targets that at
least one other pharmaceutical company was actively pursuing
• Only 8% of pharma company small molecule PCCs make it to PH III
• In 2010, only 21 new medical entities were approved by FDA
7 7
8. What is the problem?
• Regulatory hurdles too high?
• Low hanging fruit picked?
• 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
9. What is the problem?
We need a better understand disease biology
before testing proprietary compounds on sick
patients
10. What is the problem?
Most approved therapies assumed indications
represent homogenous populations
Our existing disease models often assume
pathway knowledge sufficient to infer correct
therapies
15. “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”
16.
17.
18. WHY NOT USE
“DATA INTENSIVE” SCIENCE
TO BUILD BETTER DISEASE MAPS?
19. what will it take to understand disease?
DNA RNA PROTEIN (dark ma>er)
MOVING BEYOND ALTERED COMPONENT LISTS
21. 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
21
Integrated Genetics Approaches
25. Preliminary Probalistic Models- Rosetta- Eric 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
26. Map compound signatures to disease networks
Sub-‐network contains genes Compound Gene expression
associated with diabetes traits signatures
Sub-‐network
contains genes
associated
with toxici5es
1
2
3
Sub-‐network contains genes
Tissue Disease Networks associated with obesity traits
Compound 1: Drug signature significantly enriched in subnetwork associated with diabetes traits
Compound 2: Drug signature significantly enriched in subnetwork associated with obesity traits
Compound 3: Drug signature significantly enriched in subnetwork associated with obesity traits
BUT also in subnetwork associated with toxicities
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)
..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
37. 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
39. 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
M
S
FOR
MAP
Data Tool and Disease Map Generators-
(Global coherent data sets, Cytoscape,
PLAT
Clinical Trialists, Industrial Trialists, CROs…)
NEW
RULES GOVERN RULES AND GOVERNANCE
Data Sharing Barrier Breakers-
(Patients Advocates, Governance
and Policy Makers, Funders...)
41. Sage Neuro Collaborations
Neurodegenerative
• Huntington’s Disease : Marcy MacDonald/Jim Gusella (MGH)
• $2.5M Grant to CHDI to fund generation of RNA-seq data for brain regions and peripheral tissues
for well phenotyped cohorts (also methyl genome studies)
• HD/AD/PD : MacDonald/Gusella (MGH), Myers (BU), Paulsen (Iowa)
• $6.8M RC4 grant to NIH to fund generation of SNP and RNA-seq data for brain regions from HD,
AD, PD for well phenotyped cohorts
• Alzheimer’s Disease: Green (BU), Johnson (UWM)
• Collaboration opportunity around longitudinal neuroimaging & cognitive phenotyped cohorts
(ADNI, ADGC, etc). Intersect gene expression studies. Funding for further data generation
Psychiatric
• Autism/ Schizophrenia- Consortium
Sleep & Stress
• Genetics of Sleep: Turek (Northwestern)
• Collaboration with Turek lab & Merck focused on mouse. DARPA
• Enabling Stress Resistance
• DARPA-funded collaboration with Turek lab to look at genetic and brain molecular mechanisms
that regulate physical and emotional stress responses in mouse
• Currently looking to expand to human 41
42. Alzheimer’s
Disease
• Cross-‐Pssue
coexpression
networks
for
both
normal
and
AD
brains
– prefrontal
cortex,
cerebellum,
visual
cortex
• DifferenPal
network
analysis
on
AD
and
normal
networks
• Integrate
coexpression
networks
and
Bayesian
networks
to
idenPfy
key
regulators
for
the
modules
associated
with
AD
42
43. IdenPficaPon of Disease (AD) Pathways via ComparaPve
Gene Network Analysis
40,000 genes from three Pssues
Glutathione transferase
Gain connecPvity by 91 fold
AD
(PFC, CB, VC)
nerve ensheathment
Control
Lose connecPvity by 40%
(PFC, CB, VC)
Module ConnecPvity Change (AD/Normal)
43
Bayesian Subnetworks
44. DifferenPally
Connected
Modules
in
AD
• Unfolded
protein
response
(UPR)
• AKT
HIF1
VEGF
• Olfactory
receptor
acPvity
• Sensory
percepPon
of
smell
chemical
sPmulus
• Inflammatory
Response
• Extra
cellular
matrix
(ECM)
• SynapPc
transmission
(suppressed)
• Nerve
ensheathment
44
44
44
45. Key Regulators
GlutathioneTransferase NerveEnsheathment ExtracellularMatrixPECAM1: Platelet-‐endothelial cell
adhesion molecule, a tyrosine
phosphatase acPvator that plays a
role in the platelet acPvaPon,
increased expression correlates
with MS, Crohn disease, chronic B-‐
cell leukemia, rheumatoid arthriPs,
and ulceraPve coliPs
ENPP2: Phosphodiesterase I alpha,
a lysophospholipase that acts in
chemotaxis, phosphaPdic acid
biosynthesis, regulates apoptosis
and PKB signaling; aberrant
expression is associated with
Alzheimer type demenPa, major
depressive disorder, and various
cancers
SLC22A25: solute carrier family 22,
member 25, Protein with high
similarity to mouse Slc22a19, which
is a renal steroid sulfate transporter
that plays a role in the uptake of
estrone sulfate, member of the
sugar (and other) transporter family
and the major facilitator
superfamily
Glutathione Transferase Module (Pink)
• 983 probes from all three brain regions (9% from CB, 15% from PFC and 76% from VC)
45
• Most predicPve of Braak severity score
46.
47. Why not share clinical /genomic data and model building in the
ways currently used by the software industry
(power of tracking workflows and versioning
50. 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
51. Six Pilots at Sage Bionetworks
CTCAP
Arch2POCM
The FederaPon
M
S
FOR
MAP
Portable Legal Consent
PLAT
Sage Congress Project
EW N
Ashoka/Sage MedXChange RULES GOVERN
52. CTCAP
Clinical Trial Comparator Arm Partnership “CTCAP”
Strategic Opportunities For Regulatory Science
Leadership and Action
FDA
September 27, 2011
53. 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
57. 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
60. 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)
61. 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
62. Federated
Aging
Project
:
Combining
analysis
+
narraPve
=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
69. Why not use data intensive science
to build models of disease
Current Reward Structures
Organizational Structures and Tools
Six Pilots
Opportunities