Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction

Yuan Luo*, Faraz Ahmad, Sanjiv J Shah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precision Medicine Initiative comes amid the rapid growth in quantity and modality of clinical data for HFpEF patients ranging from deep phenotypic to trans-omic data. Tensor factorization, a form of machine learning, allows for the integration of multiple data modalities to derive clinically relevant HFpEF subtypes that may have significant differences in underlying pathophysiology and differential response to therapies. Tensor factorization also allows for better interpretability by supporting dimensionality reduction and identifying latent groups of data for meaningful summarization of both features and disease outcomes. In this narrative review, we analyze the modest literature on the application of tensor factorization to related biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest multiple tensor factorization formulations capable of integrating the deep phenotypic and trans-omic modalities of data for HFpEF, or accounting for interactions between genetic variants at different omic hierarchies. We encourage extensive experimental studies to tackle challenges in applying tensor factorization for precision medicine in HFpEF, including effectively incorporating existing medical knowledge, properly accounting for uncertainty, and efficiently enforcing sparsity for better interpretability.

Original languageEnglish (US)
Pages (from-to)305-312
Number of pages8
JournalJournal of Cardiovascular Translational Research
Volume10
Issue number3
DOIs
StatePublished - Jun 1 2017

Keywords

  • Big data
  • Heart failure with preserved ejection fraction
  • Machine learning
  • Precision medicine
  • Tensor factorization
  • Trans-omics

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Pharmaceutical Science
  • Cardiology and Cardiovascular Medicine
  • Genetics(clinical)

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