Assessment of vector-host-pathogen relationships using data mining and machine learning

Agany, Diing D.M.; Pietri, Jose E.; Gnimpieba, Etienne Z.

doi:10.1016/j.csbj.2020.06.031

Cited by 34 publications

(17 citation statements)

References 59 publications

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“…We postulate that the strain-level shift in K. pneumoniae contributed to these marked alterations in ARGs and VFGs as a result of lactitol treatment. Intestinal VFGs were suggested to influence host immune homeostasis ( 41 ) and be associated with disease severity ( 42 ). Of note, we found that alterations in the composition of lactitol-treated VFGs were correlated with the strain-level changes in K.pneumonia , which accounted for shifts in the highest proportion of differentiated VFGs in the gut microbiome of patients in LC-pre vs. LC-post groups.…”

Section: Discussionmentioning

confidence: 99%

Lactitol Supplementation Modulates Intestinal Microbiome in Liver Cirrhotic Patients

et al. 2021

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Background: Cirrhosis is a common chronic liver disease characterized by irreversible diffuse liver damage. Intestinal microbiome dysbiosis and metabolite dysfunction contribute to the development of cirrhosis. Lactitol (4-β-D-galactopyranosyl-D-glucitol) was previously reported to promote the growth of intestinal Bifidobacteria. However, the effect of lactitol on the intestinal microbiome and fecal short-chain fatty acids (SCFAs) and bile acids (BAs) and the interactions among these factors in cirrhotic patients pre- and post-lactitol treatment remain poorly understood.Methods: Here, using shotgun metagenomics and targeted metabolomics methods.Results: we found that health-promoting lactic acid bacteria, including Bifidobacterium longum, B.pseudocatenulatum, and Lactobacillus salivarius, were increased after lactitol intervention, and significant decrease of pathogen Klebsiella pneumonia and associated antibiotic resistant genes /virulence factors. Functionally, pathways including Pseudomonas aeruginosa biofilm formation, endotoxin biosynthesis, and horizontal transfer of pathogenic genes were decreased in cirrhotic patients after 4-week lactitol intervention compared with before treatment.Conclusion: We identified lactitol-associated metagenomic changes, and provide insight into the understanding of the roles of lactitol in modulating gut microbiome in cirrhotic patients.

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Section: Discussionmentioning

confidence: 99%

Lactitol Supplementation Modulates Intestinal Microbiome in Liver Cirrhotic Patients

et al. 2021

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“…The first significant approach is supervised machine learning aiming to build up a predictive algorithm based on data analysis like regression and classification methods. Classification methods used in the prediction of infectious diseases in the past are “mixed linear regression (MLR)”, “artificial neural network (ANN)”, “decision tree (DT)”, “support vector machine (SVM)”, “Random forest (RF)”, “gradient boosting decision tree (GBDT)”, and “Bayesian Network (BN)” (e.g., Agany et al, 2020 ). The other AI approach is based on unsupervised machine learning, which permits computers to discover large amounts of unclassified data and learn treatment patterns.…”

Section: Resultsmentioning

confidence: 99%

Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review

Asif

Chen

Stranges

et al. 2022

Sustainable Cities and Society

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“…The research of machine learning combined with big data has played a more and more important role in the development and competition of various fields [19][20][21]. Applying machine learning to infectious disease early warning system can break through the limitations of traditional prediction system and achieve the purpose of real-time and dynamic early warning, thereby improving the effect of prevention and control [22,23]. Therefore, machine learning is used for global COVID-19 epidemic trend prediction, which is a direction that can be studied and applied.…”

Section: Related Work 21 Covid-19 Data Set Source and Analysismentioning

confidence: 99%

Research on Forecast of Global COVID-19 Development Trend

Cheng

Bai

et al. 2021

Preprint

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The rapid spread of Corona Virus Disease 2019 (COVID-2019) has seriously threatened people’s health and brought huge challenges to the medical systems of many countries. So it is necessary to predict the epidemic trend scientifically and accurately. In this paper, the development trend prediction of the global daily cumulative confirmed case, based on data from February 2, 2020 to August 14, 2021 using linear regression and nonlinear regression methods, which are auto regressive integrated moving average (ARIMA), wavelet neural network (WNN), support vector machine (SVM), recurrent neural network (RNN) and long short-term memory (LSTM). Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to evaluate the prediction accuracy of the five models. The experimental results showed that the LSTM used in this study is more accurate for predicting the development trend of global cumulative confirmed cases. And its three evaluation indexes were 0.0936%, 0.1123%, and 0.0962% respectively, which were the smallest compared with the other four models. The LSTM model was used to predict the global cumulative confirmed cases in the next five days. The prediction results showed that the global cumulative confirmed cases will rise steadily and exceed 205 million. Therefore, the health departments of various countries should take appropriate prevention and control measures in advance.

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Assessment of vector-host-pathogen relationships using data mining and machine learning

Abstract: Graphical abstract

Cited by 34 publications

References 59 publications

Lactitol Supplementation Modulates Intestinal Microbiome in Liver Cirrhotic Patients

Lactitol Supplementation Modulates Intestinal Microbiome in Liver Cirrhotic Patients

Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review

Research on Forecast of Global COVID-19 Development Trend

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