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A Data Driven Approach to Diagnosing and Treating Disease
Published on Oct 07, 20144662 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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Chapter list
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