Machine learning-based predictive modeling to identify genotypic traits associated with Salmonella enterica disease endpoints in isolates from ground chicken

Tanui, Collins Kipngetich; Karanth, Shraddha; Njage, Patrick Murigu Kamau; Meng, Jianghong; Pradhan, Abani K.

doi:10.1016/j.lwt.2021.112701

Cited by 21 publications

(14 citation statements)

References 77 publications

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“…These genes may also enhance the persistence of L. monocytogenes in different food sources. Eight out of the twenty most important genes were hypothetical genes, which is in line with the findings of prior studies [36,39]. Thus, future studies in-volving the characterization of each gene to understand its importance in L. monocytogenes adaptation and stress response along the food supply chain are warranted.…”

Section: Important Top Twenty Predictor Genessupporting

confidence: 83%

“…With the increasing usage of genome sequencing technologies, it is possible to identify genetic patterns indicative of the food source of pathogens. Recently, machine learning models have been used to identify molecular markers from foodborne pathogens linked with different hosts/phenotypes, which could be used to trace the source of human infections [26,36,37,39]. In the current study, we investigated the potential of machine learning to predict the food source origins of bacterial strains isolated from human cases of listeriosis using machine learning analyses of cgMLST data.…”

Section: Source Attribution Modelmentioning

confidence: 99%

“…Our feature-reduced cgMLST data was randomly split into a training set (70%) and a testing set (30%). Four machine learning algorithms-random forest, logit boost, stochastic gradient boosting, and support vector machine radial kernel-have been successfully applied in studies analyzing WGS data [36,37,39,76] and were therefore used in training our data. We used 10-fold cross-validation, which randomly partitions the training data set into 10 equal folds-nine folds used for model training and one fold to estimate model performance-to obtain the model with the best performance.…”

Section: Machine Learningmentioning

confidence: 99%

“…Mechanistic model-based methods are aimed at formulating simplified mathematical models to explain various phenomena by carefully examining, analyzing, and identifying patterns in relevant data [34]. On the other hand, ML focuses on 'learning' from relevant patterns in data, and using this information to make predictions [35,36]. Basically, by exploring and identifying patterns in data, ML can be used in the classification, regression, or clustering of data to draw meaningful inferences from the same.…”

Section: Introductionmentioning

confidence: 99%

“…Basically, by exploring and identifying patterns in data, ML can be used in the classification, regression, or clustering of data to draw meaningful inferences from the same. Genome sequencing information, coupled with machine learning, has been used to predict the risk of listeriosis in humans [37], the host specificity of S. enterica and E. coli [38], and host disease severity based on S. enterica gene presence/absence [36,39], and in the source attribution of S. Typhimurium [26]. With the increase in usage of genome sequencing for exploratory and integrated surveillance activities, generating massive amounts of data, as well as standardization of data collection activities (providing us with useful metadata and other useful information), machine learning and big data analytical tools become the need of the hour to provide a better understanding and improvement of current knowledge in foodborne disease epidemiology.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Model for Food Source Attribution of Listeria monocytogenes

et al. 2022

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Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.

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Section: Important Top Twenty Predictor Genessupporting

confidence: 83%

Section: Source Attribution Modelmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Model for Food Source Attribution of Listeria monocytogenes

et al. 2022

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Fractional‐calculus analysis of the dynamics of typhoid fever with the effect of vaccination and carriers

Jan,

Boulaaras,

Alnegga

et al. 2023

Int J Numerical Modelling

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Typhoid fever poses a severe hazard to public health and affects at least 27 million people annually around the globe. It is significant to conceptualize the key factors of the transmission phenomena of this bacterial infection. We formulate a novel epidemic model for typhoid fever with vaccination and carriers via Caputo‐Fabrizio operator. The fundamental results of the model are inspected analytically and the basic reproduction value are then assessed. The Jacobian matrix approach has been used to demonstrate the local stability of the infection‐free steady‐state for . We interrogated the uniqueness and existence of the solution of the recommended fractional dynamics of typhoid fever. We illustrated the solution pathways of the recommended system of typhoid fever numerically and demonstrated the dynamics of the infection with variation of different parameters. Our results demonstrated how the dynamical behavior of the typhoid fever infection is influenced by input parameters. The findings highlight the significance and persuasive behavior of fractional order and show that the model's fractional memory effects appear to be a good fit for these kinds of findings. We have shown the most attractive factors of the system for the control and prevention through our study.

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