Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches.

Sassi M, Bronsard J, Pascreau G, Emily M, Donnio PY, Revest M, Felden B, Wirth T, Augagneur Y

DOI: 10.1128/msystems.00378-22
Published: 2022 Aug 30
Container: mSystems
Publisher: Not recorded
Open access: yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
cautionDOI resolves: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1128/msystems.00378-22,
  title = {Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches.},
  author = {Sassi M and Bronsard J and Pascreau G and Emily M and Donnio PY and Revest M and Felden B and Wirth T and Augagneur Y},
  year = {2022},
  journal = {mSystems},
  doi = {10.1128/msystems.00378-22},
  url = {https://doi.org/10.1128/msystems.00378-22}
}

RIS

TY  - JOUR
TI  - Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches.
AU  - Sassi M
AU  - Bronsard J
AU  - Pascreau G
AU  - Emily M
AU  - Donnio PY
AU  - Revest M
AU  - Felden B
AU  - Wirth T
AU  - Augagneur Y
PY  - 2022
JO  - mSystems
DO  - 10.1128/msystems.00378-22
UR  - https://doi.org/10.1128/msystems.00378-22
ER  -

APA

M, S., J, B., G, P., M, E., PY, D., M, R., B, F., T, W., & Y, A. (2022). Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches.. mSystems. https://doi.org/10.1128/msystems.00378-22

Source records

pubmed · retrieved 2026-09-26T22:32:15.330Z