integrative multi-scale analysis in biomedical data science: tools, methods and challenges

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  

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

Intertumor  and  intratumor  heterogenity  

Burrell  et  al.  Nature  (2013):338–345  

Bruin  et  al,  Swanton  Science  2014  

Slide  –  thanks  to  Adam  Marcus  

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  

From  Daniel  Rubin’s  Website  

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

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

Associations

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  

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        

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

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  

• 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    

Thanks!