Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State

Adityanarayanan Radhakrishnan, Sam Freesun Friedman, Shaan Khurshid, Kenney Ng, Puneet Batra, Steven Lubitz, Anthony Philippakis, Caroline Uhler
doi: https://doi.org/10.1101/2022.05.26.493497
Adityanarayanan Radhakrishnan
1Massachusetts Institute of Technology, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sam Freesun Friedman
2Broad Institute of MIT and Harvard, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shaan Khurshid
2Broad Institute of MIT and Harvard, U.S.A.
3Massachusetts General Hospital, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kenney Ng
4IBM T.J. Watson Research Center, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Puneet Batra
2Broad Institute of MIT and Harvard, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Steven Lubitz
2Broad Institute of MIT and Harvard, U.S.A.
3Massachusetts General Hospital, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: cuhler@mit.edu aphilipp@broadinstitute.org lubitz@broadinstitute.org
Anthony Philippakis
2Broad Institute of MIT and Harvard, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: cuhler@mit.edu aphilipp@broadinstitute.org lubitz@broadinstitute.org
Caroline Uhler
1Massachusetts Institute of Technology, U.S.A.
2Broad Institute of MIT and Harvard, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: cuhler@mit.edu aphilipp@broadinstitute.org lubitz@broadinstitute.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardio-vascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results provide a framework for integrating distinct diagnostic modalities into a common representation that better characterizes physiologic state.

Competing Interest Statement

S.A.L. receives sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit / Google, Medtronic, Premier, and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. A.A.P. is a Venture Partner at GV. He has received funding from IBM, Bayer, Pfizer, Microsoft, Verily, and Intel. C.U. serves on the Scientific Advisory Board of Immunai and Relation Therapeutics and has received sponsored research support from Janssen Pharmaceuticals.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted May 28, 2022.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State
Adityanarayanan Radhakrishnan, Sam Freesun Friedman, Shaan Khurshid, Kenney Ng, Puneet Batra, Steven Lubitz, Anthony Philippakis, Caroline Uhler
bioRxiv 2022.05.26.493497; doi: https://doi.org/10.1101/2022.05.26.493497
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A Cross-Modal Autoencoder Framework Learns Holistic Representations of Cardiovascular State
Adityanarayanan Radhakrishnan, Sam Freesun Friedman, Shaan Khurshid, Kenney Ng, Puneet Batra, Steven Lubitz, Anthony Philippakis, Caroline Uhler
bioRxiv 2022.05.26.493497; doi: https://doi.org/10.1101/2022.05.26.493497

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4228)
  • Biochemistry (9107)
  • Bioengineering (6751)
  • Bioinformatics (23944)
  • Biophysics (12089)
  • Cancer Biology (9495)
  • Cell Biology (13740)
  • Clinical Trials (138)
  • Developmental Biology (7616)
  • Ecology (11661)
  • Epidemiology (2066)
  • Evolutionary Biology (15479)
  • Genetics (10618)
  • Genomics (14296)
  • Immunology (9463)
  • Microbiology (22792)
  • Molecular Biology (9078)
  • Neuroscience (48889)
  • Paleontology (355)
  • Pathology (1479)
  • Pharmacology and Toxicology (2565)
  • Physiology (3823)
  • Plant Biology (8308)
  • Scientific Communication and Education (1467)
  • Synthetic Biology (2290)
  • Systems Biology (6172)
  • Zoology (1297)