Integrative Multi-Scale Analysis in Biomedical Data Science: Tools, Methods and Challenges

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Information about Integrative Multi-Scale Analysis in Biomedical Data Science: Tools,...

Published on January 29, 2016

Author: joelhsaltz

Source: slideshare.net

1. Integra(ve  Mul(-­‐scale  Analysis   in  Biomedical  Data  Science:   Tools,  Methods  and  Challenges     Joel  Saltz   Department  of  Biomedical  Informa(cs   Stony  Brook  University CI4CC  October  2015  

2. Targeted  Therapy  against           bcr-­‐abl  -­‐-­‐    Leukemia  (CML)  

3. Intertumor  and  intratumor  heterogenity   Burrell  et  al.  Nature  (2013):338–345  

4. Bruin  et  al,  Swanton  Science  2014   Slide  –  thanks  to  Adam  Marcus  

5. MulI-­‐scale  IntegraIve  Analysis  in  Precision  Medicine   •  Predict  treatment  outcome,   select,  monitor  treatments   •  Reduce  inter-­‐observer   variability  in  diagnosis   •  Computer  assisted   exploraIon  of  new   classificaIon  schemes   •  MulI-­‐scale  cancer   simulaIons  

6. From  Daniel  Rubin’s   Website  

7. Example:  Pathology  AnalyIcal  Imaging   •  Provide   rich   informaIon   about   morphological   and   funcIonal   characterisIcs   •  Image  analysis,  feature  extracIon  on  mulIple  scales   •  SpaIally  mapped  “omics”   •  MulIple  microscopy  modaliIes   Glass Slides Scanning Whole Slide Images Image Analysis

8. Integra<ve  Morphology/”omics”   Quantitative Feature Analysis in Pathology: Emory In Silico Center for Brain Tumor Research (PI = Dan Brat, PD= Joel Saltz) NLM/NCI: Integrative Analysis/Digital Pathology R01LM011119, R01LM009239 (Dual PIs Joel Saltz, David Foran) Marcus Foundation Grant – Ari Kaufman, Joel Saltz  

9. Associations

10. Radiomics   Decoding  tumour  phenotype  by   noninvasive  imaging  using  a   quan<ta<ve  radiomics  approach   Hugo  J.  W.  L.  Aerts  et.  Al.   Nature  Communica/ons  5,   ArIcle  number:  4006  doi: 10.1038/ncomms5006   Features   Pa<ents  

11. The  Driving  Meta  Applica(on IdenIfy  and  segment  trillions  of  mulI-­‐scale  objects  from  spaIo-­‐ temporal  datasets   Extract  features  from  objects  and  spaIo-­‐temporal  regions   Support  queries  against  ensembles  of  features  extracted  from  mulIple   datasets   StaIsIcal  analyses  and  machine  learning  to  link  features  to  physical   and  biological  phenomena   Feature  driven  simulaIon  –    use  extracted  features  as  simulaIon  iniIal,   boundary  condiIons  and  to  assimilate  data  into  simulaIons      

12. Detect and track changes in data during production Invert data for reservoir properties Detect and track reservoir changes Assimilate data & reservoir properties into the evolving reservoir model Use simulation and optimization to guide future production Example:  Oil  Field  Management  –  Joint  NSF  ITR  with  Mary  Wheeler,  Paul  Stoffa

13. Commonali(es  with  Physical  Science  and  Engineering • MulI-­‐scale    material/Issue  structural,  molecular,  funcIonal   characterizaIon.    Design  of  materials  with  specific  structural,   energy  storage    properIes,  brain,  regeneraIve  medicine,   cancer   • IntegraIve  mulI-­‐scale  analyses  of  the  earth,  oceans,   atmosphere,  ciIes,  vegetaIon  etc    –  cameras  and  sensors  on   satellites,  aircra^,  drones,  land  vehicles,  staIonary  cameras   • Digital  astronomy     • Hydrocarbon  exploraIon,  exploitaIon,  polluIon  remediaIon  

14. • Aerospace  –  wind  tunnels,  acquisiIon  of  data  during   flight   • Solid  prinIng  integraIve  data  analyses   • Autonomous  vehicles,  e.g.  self  driving  cars   • Data  generated  by  numerical  simulaIon  codes  –  PDEs,   parIcle  methods   • Mul$-­‐scale  Precision  Medicine    

15. Thanks!

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