Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis
Author:
Funder
National Institutes of Health
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00404-023-07360-7.pdf
Reference34 articles.
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2. Alcaide ML, Rodriguez VJ, Fischl MA, Jones DL, Weiss SM (2017) Addressing intravaginal practices in women with HIV and at-risk for HIV infection, a mixed methods pilot study. Int J Womens Health 9:123–132. https://doi.org/10.2147/IJWH.S125883
3. Alduhaidhawi AHM, AlHuchaimi SN, Al-Mayah TA, Al-Ouqaili MTS, Alkafaas SS, Muthupandian S, Saki M (2022) Prevalence of CRISPR-cas systems and their possible association with antibiotic resistance in Enterococcus faecalis and Enterococcus faecium collected from hospital wastewater. Infect Drug Resist 15:1143–1154
4. Beck D, Foster JA (2014) Machine learning techniques accurately classify microbial communities by bacterial vaginosis characteristics. PLoS ONE 9(2):e87830
5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
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