On the Classification Consistency of High-Dimensional Sparse Neural Network

Yang, Kaixu; Maiti, Taps

doi:10.1109/dsaa.2019.00032

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…In the machine learning framework, we assessed three feature selection methods (logistic regression, LASSO, a neural-network-based approach ENNS 41 ) along with three tree-based classifiers: Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Our approach limited each feature selection method to choose a maximum of ten features to mitigate the risk of overfitting and, more importantly, to reduce the time and effort required to measure the selected features in the laboratory (see Discussion).…”

Section: Resultsmentioning

confidence: 99%

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Echinocandin heteroresistance causes prophylaxis failure and facilitates breakthroughCandida parapsilosisinfection

Zhai

Liao

Jaggavarapu

et al. 2022

Preprint

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Antimicrobial drug resistance is a major problem in the prevention and treatment of infectious diseases. For patients on antimicrobial prophylaxis during cancer treatment and organ transplantation, breakthrough infections represent a significant and often unexplained cause of morbidity. Here, in high-risk patients on micafungin prophylaxis, we reveal that heteroresistance, the presence of a phenotypically unstable, low frequency subpopulation of resistant cells (~1 in 10,000), causes breakthrough bloodstream infections by Candida parapsilosis strains that were misclassified as susceptible by standard antimicrobial susceptibility testing. Genome-matched micafungin heteroresistant strains were isolated from the intestinal reservoir prior to invasive disease. To identify heteroresistance, we harnessed whole genome sequence analysis of 118 clinical C. parapsilosis strains and extracted six genetic features into a predictive machine learning model that distinguished heteroresistant strains with high accuracy. These data directly implicate heteroresistance in unexplained prophylaxis failure and highlight a proof-of-principle approach to detect this phenomenon and inform clinical decisions.

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Section: Resultsmentioning

confidence: 99%

“…When the number of non-zero features was fewer than “n_features”, all non-zero features were selected. The ENNS algorithm has been implemented previously 41 , see code at https://github.com/KaixuYang/ENNS.…”

Section: Methodsmentioning

confidence: 99%