Journal: PLoS ONE

Article Title: Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

doi: 10.1371/journal.pone.0204644

Figure Lengend Snippet: The sequence minimization optimization (SMO) and Random Forest (RF) classifier models showed greater predictive accuracy than the Naïve Bayesian (NB) and Voted Perceptron (VP) classifier models.

Article Snippet: A metanode in the KNIME workflow ( ) was designed to build the various classifier models that were earlier mentioned (i.e. Naïve Bayesian classifier, Sequential Minimization Optimization (SMO) classifier, Random Forest (RF) classifier and Voted perceptron (VP) classifier).

Techniques: Sequencing

Journal: PLoS ONE

Article Title: Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

doi: 10.1371/journal.pone.0204644

Figure Lengend Snippet: Evaluation parameters from the prediction of bioactivity class of an independent NAA test dataset by the four classifier models used in this study.

Article Snippet: A metanode in the KNIME workflow ( ) was designed to build the various classifier models that were earlier mentioned (i.e. Naïve Bayesian classifier, Sequential Minimization Optimization (SMO) classifier, Random Forest (RF) classifier and Voted perceptron (VP) classifier).

Techniques: Sequencing, Plasmid Preparation

Journal: PLoS ONE

Article Title: Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

doi: 10.1371/journal.pone.0204644

Figure Lengend Snippet: The sequence minimization optimization (SMO) and Random Forest (RF) classifier models showed greater predictive accuracy than the Naïve Bayesian (NB) and Voted Perceptron (VP) classifier models.

Article Snippet: Four classifier models were trained with natural products with in-vitro antiplasmodial activities (NAA) (using Weka version 3.6 node in KNIME): Voted Perceptron (VP), Naïve Bayesian (NB), Random Forest (RF) and Sequential Minimization Optimization (SMO).

Techniques: Sequencing

Journal: PLoS ONE

Article Title: Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

doi: 10.1371/journal.pone.0204644

Figure Lengend Snippet: Evaluation parameters from the prediction of bioactivity class of an independent NAA test dataset by the four classifier models used in this study.

Article Snippet: Four classifier models were trained with natural products with in-vitro antiplasmodial activities (NAA) (using Weka version 3.6 node in KNIME): Voted Perceptron (VP), Naïve Bayesian (NB), Random Forest (RF) and Sequential Minimization Optimization (SMO).

Techniques: Sequencing, Plasmid Preparation

Journal: Journal of Medical Internet Research

Article Title: AI Applications for Chronic Condition Self-Management: Scoping Review

doi: 10.2196/59632

Figure Lengend Snippet: Summary of included studies (N=66).

Article Snippet: Tsang et al [ ], (2020) , United Kingdom , Asthma , System validity testing , ML (decision tree, logistic regression, naïve Bayesian, and SVM) , ML algorithms classify whether a patient is stable or unstable and allow early warning of asthma aggravation. , Prediction accuracy of both logistic regression and naïve Bayesian algorithm was the best at 0.87..

Techniques: Control, Activity Assay, Injection, Biomarker Discovery, Medications, Sequencing, Software, Clinical Proteomics, Concentration Assay, Functional Assay, Infection