A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.

Ahmad RM, Ali BR, Al-Jasmi F, Sinnott RO, Al Dhaheri N, Mohamad MS

DOI: 10.1093/bib/bbad479
Published: 2023 Nov 22
Container: Briefings in bioinformatics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1093/bib/bbad479,
  title = {A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.},
  author = {Ahmad RM and Ali BR and Al-Jasmi F and Sinnott RO and Al Dhaheri N and Mohamad MS},
  year = {2023},
  journal = {Briefings in bioinformatics},
  doi = {10.1093/bib/bbad479},
  url = {https://doi.org/10.1093/bib/bbad479}
}

RIS

TY  - JOUR
TI  - A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.
AU  - Ahmad RM
AU  - Ali BR
AU  - Al-Jasmi F
AU  - Sinnott RO
AU  - Al Dhaheri N
AU  - Mohamad MS
PY  - 2023
JO  - Briefings in bioinformatics
DO  - 10.1093/bib/bbad479
UR  - https://doi.org/10.1093/bib/bbad479
ER  -

APA

RM, A., BR, A., F, A., RO, S., N, A. D., & MS, M. (2023). A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.. Briefings in bioinformatics. https://doi.org/10.1093/bib/bbad479

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pubmed · retrieved 2026-09-25T06:54:53.610Z