GA-based selection of vaginal microbiome features associated with bacterial vaginosis

Carter, Joi; Beck, Daniel; Williams, Henry; Dozier, Gerry; Foster, James A.

doi:10.1145/2576768.2598378

Cited by 8 publications

(8 citation statements)

References 24 publications

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“…Although Amsel's criteria and the Nugent scoring system are considered as the “gold standard” for BV diagnosis, problems still exist because of the “interobserver variability” and the fact that the “intermediate vaginal microbiome” do not necessarily indicate disease progression to BV or vice versa (van de Wijgert et al, 2014 ). The advances in machine learning and its application in other fields have been followed by attempts to apply computer algorithms in BV diagnosis (Baker et al, 2014 ; Beck and Foster, 2014 , 2015 ; Carter et al, 2014 ; Song et al, 2017 ; Jarvis et al, 2018 ).…”

Section: Alternative Approaches As Potential Diagnostic Avenues For Tmentioning

confidence: 99%

“…Computer algorithms could potentially have a wide range of applications that may help clinicians and researchers to search for models that identify features relevant to BV diagnosis, to assess relative bacterial abundance data (qPCR) to diagnose BV, or to analyze bacterial morphotypes on microscope images for more accurate Nugent scoring results (Beck and Foster, 2014 , 2015 ; Carter et al, 2014 ; Song et al, 2017 ; Jarvis et al, 2018 ). One of the first attempts to apply machine learning algorithms in BV diagnosis was done by Beck and Foster ( 2014 ), where the authors first grouped the correlations in microbial relative abundance data from studies by Ravel et al ( 2011 ) and Srinivasan et al ( 2012 ) and built different classification models (based on Amsel's criteria or Nugent scoring) using three different types of machine learning algorithms [“genetic programming” (GP), “logistic regression” (LR) and “random forest” (RF)].…”

Section: Alternative Approaches As Potential Diagnostic Avenues For Tmentioning

confidence: 99%

See 1 more Smart Citation

Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities

Redelinghuys

Geldenhuys

Jung

et al. 2020

Front. Cell. Infect. Microbiol.

113

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A healthy female genital tract harbors a microbiome dominated by lactic acid and hydrogen peroxide producing bacteria, which provide protection against infections by maintaining a low pH. Changes in the bacterial compositions of the vaginal microbiome can lead to bacterial vaginosis (BV), which is often associated with vaginal inflammation. Bacterial vaginosis increases the risk of acquiring sexually transmitted infections (STIs) like human immunodeficiency virus (HIV) and affects women's reproductive health negatively. In pregnant women, BV can lead to chorioamnionitis and adverse pregnancy outcomes, including preterm premature rupture of the membranes and preterm birth. In order to manage BV effectively, good diagnostic procedures are required. Traditionally clinical and microscopic methods have been used to diagnose BV; however, these methods require skilled staff and time and suffer from reduced sensitivity and specificity. New diagnostics, including highly sensitive and specific point-of-care (POC) tests, treatment modalities and vaccines can be developed based on the identification of biomarkers from the growing pool of vaginal microbiome and vaginal metabolome data. In this review the current and future diagnostic avenues will be discussed.

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Section: Alternative Approaches As Potential Diagnostic Avenues For Tmentioning

confidence: 99%

Section: Alternative Approaches As Potential Diagnostic Avenues For Tmentioning

confidence: 99%

Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities

Redelinghuys

Geldenhuys

Jung

et al. 2020

Front. Cell. Infect. Microbiol.

113

View full text Add to dashboard Cite

show abstract

“…The GAs have also been modified and improved to adapt to different computational environments and for different applications [26, 27]. Carter et al [28] applied GA to their study to select vaginal microbiome features associated with bacterial vaginosis. However, the actual features were not reported, as authors explained that evaluation was needed from both microbial and clinical perspectives in the future.…”

Section: Methodsmentioning

confidence: 99%

Selection of microbial biomarkers with genetic algorithm and principal component analysis

et al. 2019

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BackgroundPrincipal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to determine whether individuals can be clustered into two classification groups based on pre-determined criteria: control and disease group. However, a combination of other components may exist which better distinguish diseased individuals from healthy controls. Genetic algorithms (GAs) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and a genetic algorithm (GA) for identifying sets of bacterial species associated with obesity and metabolic syndrome (Mets).ResultsThe prediction models built using the combination of principal components (PCs) selected by GA were compared to the models built using the top PCs that explained the most variance in the sample and to models built with selected original variables. The advantages of combining PCA with GA were demonstrated.ConclusionsThe proposed algorithm overcomes the limitation of PCA for data analysis. It offers a new way to build prediction models that may improve the prediction accuracy. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.

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“…The GA algorithms have also been modified and improved to adapt to different computational environments and for different applications [23,24]. An application of GA for for selecting vaginal microbiome features associated with bacterial vaginosis was found in [25]. However, the actual features were not reported as authors explained that evaluation was needed from both microbial and clinical perspectives in the future.…”

Section: B Genetic Algorithmsmentioning

confidence: 99%

Combination of Principal Component Analysis and Genetic Algorithm for Microbial Biomarker Identification in Obesity

Zhang

West

Chen

et al. 2018

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Background: A large number of microbial species have been detected in human faecal samples, with many of the species having high correlations with each other. Principal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to see whether individuals can be clustered into two classification groups based on predetermined criteria: control and disease group. However, there might be a combination of other components that better distinguish diseased individuals from healthy controls. Genetic algorithms (GA) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and GA for identifying sets of bacterial species associated with high body mass. Results: GA has selected the subsets of the principal components (PCs) produced by PCA. The prediction models built with theses PCs produced much higher area under the curve (AUC) values compared to the models built using topPCs which explained the most variance in the sample. The combinatorial effect of the identified bacterial species that contributed the most to the PCs may be associated with body mass. Conclusions:The proposed algorithm overcomes the limitation of using PCA for prediction modelling. The application of the algorithm on an obesity study has shown the value of applying GA for selecting PC subsets from PCA to improve prediction models. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.

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GA-based selection of vaginal microbiome features associated with bacterial vaginosis

Cited by 8 publications

References 24 publications

Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities

Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities

Selection of microbial biomarkers with genetic algorithm and principal component analysis

Combination of Principal Component Analysis and Genetic Algorithm for Microbial Biomarker Identification in Obesity

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