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bioRxiv · 10.1101/2025.03.26.645597

Imputing missing minimum inhibitory concentration (MIC) values for Pseudomonas aeruginosa strains with a Denoising AutoEncoder

Denakpo, E.·Dias, N.·Pitout, J.·Naas, T.·Pillai, D. R.·Jay, F.·Iorga, B. I.
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Abstract

Pseudomonas aeruginosa is a problematic pathogen with complex antibiotic resistance patterns. In clinical practice, minimum inhibitory concentration (MIC) tests typically focus on a limited subset of antibiotics, hindering a comprehensive assessment of a strains resistance profile. Here, we introduce MICFiller, a Denoising AutoEncoder (DAE) model designed to impute missing MIC values for 14 antibiotics in Pseudomonas aeruginosa within a specific dilution range by leveraging known MIC measurements for other antibiotics in the same strain. We evaluated the performance of DAE against two other commonly used methods: Multiple Imputation by Chained Equations (MICE) and simple median imputation. The DAE achieved the highest balanced 1-tier accuracy for most antibiotics, with performance closely matching that of MICE. MICFiller is freely accessible through a user-friendly web interface at http://iorgalab.org:4567/micfiller, offering clinicians a more complete view of a strains antibiotic resistance profile.

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Denakpo, E., Dias, N., Pitout, J., Naas, T., Pillai, D. R., Jay, F., Iorga, B. I.. 2025-03-26. Imputing missing minimum inhibitory concentration (MIC) values for Pseudomonas aeruginosa strains with a Denoising AutoEncoder. https://doi.org/10.1101/2025.03.26.645597

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