Additional file 4: Figure S4. Monte Carlo simulation to assess the effect of randomly sampling from the unlabelled class on the classifier performance. Ten thousands random samples of the unlabelled class were aggregated to the positive class and used to train and test a NN classifier. Histograms show distributions of (A) accuracy (mean = 0.71, standard deviation = 0.02) and (B) AUC (mean = 0.77, standard deviation = 0.02) calculated using the test set
Statistical classification is a critical component of utilizing metabolomics data for examining the ...
After completion of the Human Genome Project, disease targets at the molecular level can be identifi...
Abstract Background Data generated using 'omics' technologies are characterized by high dimensionali...
Additional file 7: Figure S5. Permutation test to assess the significance of the literature-based va...
<p>The full dataset is a gene expression matrix with 8,000 features (the genes) as rows and 30 sampl...
Model-based prediction is dependent on many choices ranging from the sample collection and predictio...
In this study, two probabilistic machine-learning algorithms were compared for in silico target pred...
Model-based prediction is dependent on many choices ranging from the sample collection and predictio...
Machine learning and statistical model based classifiers have increasingly been used with more compl...
Machine learning and statistical model based classifiers have increasingly been used with more compl...
A major cause of failed drug discovery programs is suboptimal target selection, resulting in the dev...
In this study, two probabilistic machine-learning algorithms were compared for in silico target pred...
<p>(<b>A</b>) The four heatmaps show the predictions resulting from diverse binary classifiers for t...
<p><b>Copyright information:</b></p><p>Taken from "Prediction of potential drug targets based on sim...
Motivation: Class predicting with gene expression is widely used to generate diagnostic and/or progn...
Statistical classification is a critical component of utilizing metabolomics data for examining the ...
After completion of the Human Genome Project, disease targets at the molecular level can be identifi...
Abstract Background Data generated using 'omics' technologies are characterized by high dimensionali...
Additional file 7: Figure S5. Permutation test to assess the significance of the literature-based va...
<p>The full dataset is a gene expression matrix with 8,000 features (the genes) as rows and 30 sampl...
Model-based prediction is dependent on many choices ranging from the sample collection and predictio...
In this study, two probabilistic machine-learning algorithms were compared for in silico target pred...
Model-based prediction is dependent on many choices ranging from the sample collection and predictio...
Machine learning and statistical model based classifiers have increasingly been used with more compl...
Machine learning and statistical model based classifiers have increasingly been used with more compl...
A major cause of failed drug discovery programs is suboptimal target selection, resulting in the dev...
In this study, two probabilistic machine-learning algorithms were compared for in silico target pred...
<p>(<b>A</b>) The four heatmaps show the predictions resulting from diverse binary classifiers for t...
<p><b>Copyright information:</b></p><p>Taken from "Prediction of potential drug targets based on sim...
Motivation: Class predicting with gene expression is widely used to generate diagnostic and/or progn...
Statistical classification is a critical component of utilizing metabolomics data for examining the ...
After completion of the Human Genome Project, disease targets at the molecular level can be identifi...
Abstract Background Data generated using 'omics' technologies are characterized by high dimensionali...