Active Projects
Theme 1 Scalable ML algorithms for high-dimensional data
Theme 2 Predictive medicine
Theme 3 Biomarker discovery, personalized medicine
Theme 4 Molecular biology, evolutionary biology, cancer biology
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Machine learning in the operating room
about: The goal of this project is to develop a ML system to predict respiratory events from surgery and anesthesia in real time, based on time-series measurements of patient's condition during the surgery
people: Scott Lundberg, Su-In Lee, Jerry Kim, Bala Nair, Shu-Fang Newman
funding: ITHS, eScience
Efficient dimensionality reduction for high-dimensional network estimation about: A
For more details, please read Celik et al. presented at the NIPS Workshop on MLCB 2013 and ICML 2014.
people: Safiye Celik, Benjamin Logsdon, Su-In Lee
funding: NSF, AAUW (Safiye's fellowship), NIH
A probabilistic framework for meta-analysis of high-dimensional network inference about:
people: Safiye Celik, Su-In Lee
funding: NSF
Identifying molecular markers for tumor resectability about:
people: Benjamin Logsdon, Su-In Lee, David Hawkins, Stephanie Battle, Mara Rendi, Charles Drescher
funding: Solid Tumor Translational Research, NIH
Network-based classification of driver vs. passenger mutations about:
people: Patricio Velez, Su-In Lee
funding: NIH
An unsupervised feature learning approach to classify cancer patients about:
people: Safiye Celik, Su-In Lee
funding: Solid Tumor Translational Research
Identifying expression drivers in cancer about:
people: Benjamin Logsdon, Su-In Lee, Andrew Gentles, Chris Miller, Tony Blau, Pamela Becker
funding: NIH
A big data approach to identify molecular markers for chemotherapy drugs in AML
about:
people: Benjamin Logsdon, Su-In Lee, Vivian, Oehler, Chris Miller, Tony Blau, Pamela Becker
funding: NIH, Life Sciences Discovery Fund
and many more (under construction)...
 cancer systems biology:
learning biological networks that can define cancer subtypes and predict treatment outcomes.
Ben Logsdon, Su-In Lee
collaborators: Andrew Gentles (Stanford), David Hawkins (Genome Sciences), Tony Blau (Medicine), Pay Monnat (Pathology).
 personalized cancer treatment: building a prediction model to select the best chemotherapy drugs for individual patients.
collaborators: Tony Blau (Medicine), Pam Becker (Medicine).
 systems genetics: identifying the complex path from genotype to phenotype.
collaborators: Daphne Koller (Stanford), Sulin Wu (ULCA), Tom Drake (UCLA), Jake Lusis (UCLA)
 understanding the effect of hormone therapy on gene networks
Ben Logsdon, Su-In Leecollaborators: Nick Smith, Bruce Psaty, Astrid Suchy-Dicey (Cardiovascular Health Research Unit)
 human-friendly machine learning
Danielle Bragg, James Forgarty, Su-In Lee
 retreving information from 3D image data for medical diagnosis
Ezgi Mercan, Linda Shapiro, Su-In Lee.
collaborators: medical investigators at U of Pittsburgh
 classification and interest region localization on craniosynostosis skulls
Lynn Yang, Su-In Lee, Linda Shapiro
collaborators: Michael Cunningham, Matthew Speltz, Craig Birgfeld, Indriyati Atmosukarto (Seattle Children's Research Institute).
 predictive medicine: applying ML techniques for better prediction of patients' sudden condition changes.
collaborators: Meliha Yetisgen-Yildiz (BHI), Heather Evans (Dept. of Surgery), Emily Fox (Statistics).
 genetics of Alzheimer's disease and personalized treatment
Ben Logsdon, Su-In Lee
collaborators: Tom Montine (Neuropathology), Paul Crane (UW Medicine).
 identifying driver pathways in cancer
Maxim Grechkin, Su-In Lee
collaborators: Andrew Gentles (Stanford), Tony Blau (Medicine), Ray Monnat (Pathology).
 learning interaction between different layers in gene regulation
Patricio Velez, Larry Ruzzo, Su-In Lee
collaborators: Tony Blau (UW Medicine), Ray Monnat (Pathology).
 network-based understanding of complex diseases and treatment outcomes
Sonya Alexandrova, Su-In Lee
collaborators: Nathan Price (ISB).
 understanding the genetic and molecular basis of drug sensitivity phenotype
Peter Ney, Su-In Lee
collaborators: Maitreya Dunham (Genome Sciences), Tony Blau (UW Medicine), Ray Monnat (Pathology).
 sparse learning of very large graphical models
Safiye Celik, Ben Logsdon, Su-In Lee
 structured joint graphical lasso
Karthik Mohan, Mike Chung, Seungyeop Han, Su-In Lee.
collaborators: Daniela Witten (Biostatistics), Maryam Fazel (EE)
 sparse learning of Gaussian graphical models
Karthik Mohan, Palma London, Su-In Lee.
collaborators: Daniela Witten (Biostatistics), Maryam Fazel (EE)
and more.
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