Exploring the latent space of integrated heterogeneous data sources in breast cancer
Principal Investigator
Name
David Longo
Degrees
A.L.M.
Institution
Emergent Dynamics
Position Title
Chief Executive Officer
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-623
Initial CDAS Request Approval
May 12, 2020
Title
Exploring the latent space of integrated heterogeneous data sources in breast cancer
Summary
Emergent Dynamics intends to explore the latent space of breast cancer data run through variational autoencoders with deconfounding. We intend to elucidate the underlying causes and contributors to various subtypes of breast cancer. The work takes in expression data in the form of RNASeq, clinical data, and CNA and encodes those input sources down to a latent space that can be walked to evaluate deep connections between data points. Finally, the project aims to construct a state of the art data generator for modeling cancer.
Aims
- Encode breast cancer data into a walkable latent space
- Elucidate key contributors in cancer progression
- Establish state of the art data generation models to model cancer
Collaborators
David Longo, Emergent Dynamics
Bara Badwan, Emergent Dynamics
Chris Zoumadakis, Emergent Dynamics
Nancy Parmalee, Emergent Dynamics
John Lazar, Emergent Dynamics
Tom Murray, Emergent Dynamics
Aly Abdelkareem, Emergent Dynamics
Matthias Denecke, Emergent Dynamics
Andrew Brown, Emergent Dynamics