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A Data Driven Approach to Diagnosing and Treating Disease
Published on 2014-10-074678 Views
Throughout the biomedical and life sciences research community, advanced integrative biology algorithms are employed to integrate large scale data across many different high-dimensional datatypes to c
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Presentation
A Data Driven Approach to Diagnosing and Treating Disease00:00
Orthodoxies00:02
Famous Quotes00:57
Is medicine poised for a fundamental transformation?01:23
Team Oracle and Big Data at America’s Cup01:30
Team Honda and Big Data at Indy02:17
What about new born screening in the state of New York?02:37
We are on the crest of a tsunami in consumer sensor technologies - 103:45
We are on the crest of a tsunami in consumer sensor technologies - 204:15
Printable tattoo biosensor04:18
Pixie Scientific04:30
theranos04:41
Team NSA, a truly big data endeavor05:14
Even the great city of New York is in on the game05:25
WellnessFX05:57
Considering the digital universe of data to better diagnose and treat patients06:40
Multiscale measures of patients now available through efforts like Mount Sinai’s Biobank (>25,000 *identified* patients and growing fast)07:00
That promise to enable the construction of molecular networks that define the biological processes that comprise living systems 07:32
Thinking outside of the box: The Candle Problem - 108:43
Thinking outside of the box: The Candle Problem - 209:44
Thinking outside of the box: The Candle Problem - 310:02
Many examples of others having successfully broken “Orthodoxies”10:13
These technologies are enabling scoring of very large-scale, high-dimensional data on individuals for low cost11:27
Integrating data to build predictive models of living systems11:47
Computational Infrastructure12:58
Building networks from high-dimensional data scored in populations13:31
Establishing causality14:08
Mendelian Randomization as a Path to Causal Inference14:57
Integrating all data to predictive network models of living systems - 115:49
Integrating all data to predictive network models of living systems - 216:38
Toward whole-cell models for science and engineering - 116:55
Toward whole-cell models for science and engineering - 217:55
Toward whole-cell models for science and engineering - 319:26
Organizing 163 genetic loci for IBD20:21
Constructing predictive network models for IBD - 121:53
Constructing predictive network models for IBD - 222:59
From these causal network structures we can identify points of therapeutic intervention - 123:41
From these causal network structures we can identify points of therapeutic intervention - 224:25
Integrated Systems Approach identifies Genetic Nodes and Networks in Late-Onset Alzheimers's Disease24:27
Connections between diseases and tissues: IBD network driving Alzheimer’s 25:13
The microglia pathogen phagocytosis pathway26:54
Constructing the co-expression networks27:40
Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers28:20
Core disease modules harbor pluripotent drug targets31:02
The predictive network models we will construct will enable stratification of patient populations32:40
Integrating diverse data for psychiatric disease to get at predictive models of these diseases33:24
Identify regions of interest from cases and controls - 133:50
Identify regions of interest from cases and controls - 234:33
Making the link between imaging and molecular data35:31
Constructing a new map of the Allen Brain Atlas36:06
To link imaging data we start with a map of 115 regions of interest constructed using Ayasdi’s topological data analysis platform37:13
Regions identified as differential between cases and controls37:53
Now project the imaging graph into the gene expression graph - 138:04
Now project the imaging graph into the gene expression graph - 238:14
From these graphs we can project the genes comprising the pathways enriched in these regions to predictive network models38:17
A network informed view of schizophrenia - 138:19
A network informed view of schizophrenia - 239:00
A network informed view of schizophrenia - 339:24
Modeling SZ with hiPSC neural cells - 139:46
Modeling SZ with hiPSC neural cells - 240:12
Modeling SZ with hiPSC neural cells - 340:14
Modeling SZ with hiPSC neural cells - 440:15
Screening networks for novel drug discovery40:56
Acknowledgements42:32