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Evaluations and practical usage show that our proposed solution is very efficient in terms of availability, performance, and the deep learning based approach has better prediction accuracy than that of both classical SVR based approach and the latest XGBoost approach.
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Simulation results show that the proposed method in comparison with a genetic algorithm-based approach has better accuracy and higher performance.
Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard χ2 test.
Regarding the number of tweets and users, the activity-based approach has better coverage compared to the random samples.
Furthermore, all the analysis results above suggest our stringent homology-based approach has better performance than the conventional homology-based approach in predicting host-pathogen PPIs.
The comparison shows that our stringent homology-based approach has better performance in predicting more relevant and meaningful host-pathogen PPI than the conventional approach.
Pathway enrichment analysis, functional enrichment analysis, disease-related gene list enrichment analysis, etc. all support the plausibility of our prediction results and show that our stringent homology-based approach has better performance in predicting H. sapiens M.
The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity.
The AHP-MCR approach has better precision than standard user-based CF and TF-IDF based CBF.
These assessments show that our stringent DDI-based approachasas better performance in predicting PPIs than the conventional approach.
Totally, the proposed approach has better predicting performance.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com