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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "SCFA" in publications use:'
type: software
license: LGPL-2.0-only
title: 'SCFA: SCFA: Subtyping via Consensus Factor Analysis'
version: 1.15.0
abstract: Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy
  signals from consistent molecular patterns in multi-omics data. SCFA first uses
  an autoencoder to select only important features and then repeatedly performs factor
  analysis to represent the data with different numbers of factors. Using these representations,
  it can reliably identify cancer subtypes and accurately predict risk scores of patients.
authors:
- family-names: Tran
  given-names: Duc
  email: duct@nevada.unr.edu
- family-names: Nguyen
  given-names: Hung
preferred-citation:
  type: article
  title: A Novel Method for Cancer Subtyping and Risk Prediction Using Consensus Factor
    Analysis
  authors:
  - family-names: Tran
    given-names: Duc
    email: duct@nevada.unr.edu
  - family-names: Nguyen
    given-names: Hung
  - family-names: Le
    given-names: Uyen
  - family-names: Bebis
    given-names: George
  - family-names: Luu
    given-names: Hung N.
  - family-names: Nguyen
    given-names: Tin
  year: '2020'
  journal: Frontiers in Oncology
  url: https://www.frontiersin.org/articles/10.3389/fonc.2020.01052
repository: https://bioc.r-universe.dev
repository-code: https://github.com/duct317/SCFA
url: https://github.com/duct317/SCFA
contact:
- family-names: Tran
  given-names: Duc
  email: duct@nevada.unr.edu