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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "sivs" in publications use:'
type: software
license: GPL-3.0-only
title: 'sivs: Stable Iterative Variable Selection'
version: 0.2.10
doi: 10.1093/bioinformatics/btab501
identifiers:
- type: doi
  value: 10.32614/CRAN.package.sivs
abstract: An iterative feature selection method (manuscript submitted) that internally
  utilizes various Machine Learning methods that have embedded feature reduction in
  order to shrink down the feature space into a small and yet robust set.
authors:
- family-names: Mahmoudian
  given-names: Mehrad
  email: m.mahmoudian@gmail.com
  orcid: https://orcid.org/0000-0001-7650-1862
- family-names: Venäläinen
  given-names: Mikko
  orcid: https://orcid.org/0000-0003-1777-4259
- family-names: Klèn
  given-names: Riku
  orcid: https://orcid.org/0000-0002-0982-8360
- family-names: Elo
  given-names: Laura
  orcid: https://orcid.org/0000-0001-5648-4532
preferred-citation:
  type: article
  title: Stable Iterative Variable Selection
  authors:
  - family-names: Mahmoudian
    given-names: Mehrad
    email: mehrad.mahmoudian@utu.fi
    orcid: https://orcid.org/0000-0001-7650-1862
  - family-names: Venäläinen
    given-names: Mikko
    orcid: https://orcid.org/0000-0003-1777-4259
  - family-names: Klèn
    given-names: Riku
    orcid: https://orcid.org/0000-0002-0982-8360
  - family-names: Elo
    given-names: Laura
    orcid: https://orcid.org/0000-0001-5648-4532
  journal: Bioinformatics
  year: '2021'
  month: '7'
  issn: 1367-4803
  doi: 10.1093/bioinformatics/btab501
  url: https://doi.org/10.1093/bioinformatics/btab501
  notes: R package is available via https://cran.r-project.org/package=sivs
  abstract: The emergence of datasets with tens of thousands of features, such as
    high-throughput omics biomedical data, highlights the importance of reducing the
    feature space into a distilled subset that can truly capture the signal for research
    and industry by aiding in finding more effective biomarkers for the question in
    hand. A good feature set also facilitates building robust predictive models with
    improved interpretability and convergence of the applied method due to the smaller
    feature space.Here, we present a robust feature selection method named Stable
    Iterative Variable Selection (SIVS) and assess its performance over both omics
    and clinical data types. As a performance assessment metric, we compared the number
    and goodness of the selected feature using SIVS to those selected by LASSO regression.
    The results suggested that the feature space selected by SIVS was, on average,
    41\% smaller, without having a negative effect on the model performance. A similar
    result was observed for comparison with Boruta and Caret RFE.The method is implemented
    as an R package under GNU General Public License v3.0 and is accessible via Comprehensive
    R Archive Network (CRAN) via https://cran.r-project.org/web/packages/sivs/index.html
    or through Github via https://github.com/mmahmoudian/sivs/Supplementary data are
    available at Bioinformatics online.
repository: https://mmahmoudian.r-universe.dev
repository-code: https://github.com/mmahmoudian/sivs
commit: 1fed9d20e5e45b0238178b3f524f546ca29a4f2b
url: https://doi.org/10.1093/bioinformatics/btab501
date-released: '2023-10-31'
contact:
- family-names: Mahmoudian
  given-names: Mehrad
  email: m.mahmoudian@gmail.com
  orcid: https://orcid.org/0000-0001-7650-1862
references:
- type: manual
  title: 'sivs: Stable Iterative Variable Selection'
  authors:
  - family-names: Mahmoudian
    given-names: Mehrad
    email: mehrad.mahmoudian@utu.fi
    orcid: https://orcid.org/0000-0001-7650-1862
  - family-names: Venäläinen
    given-names: Mikko
    orcid: https://orcid.org/0000-0003-1777-4259
  - family-names: Klèn
    given-names: Riku
    orcid: https://orcid.org/0000-0002-0982-8360
  - family-names: Elo
    given-names: Laura
    orcid: https://orcid.org/0000-0001-5648-4532
  year: '2023'
  notes: R package version 0.2.10
  url: https://CRAN.R-project.org/package=sivs