<p>Results obtained with individual machine-learning tools, trained on the Wright et al. data set and using 20 classifier genes are shown. Survival separation between ABC and GCB classes for the data sets GSE32918, and GSE10846 divided into CHOP and R-CHOP components, was used for assessment. Hazard Ratios were generated for GCB relative to ABC as baseline. The classifiers were ordered by their average rank across the data sets; with rank determined by the p-value of the ABC/GCB separation. The LPS classifier was used for comparison with either a 0.8 or 0.9 p-value cut-off, with either MaxAvgMerge or MedianMerge methods of combining probes (see Materials and Methods). The Classifier Identity, Hazard Ratio (GCB vs ABC as baseline), 95% confi...
PurposeMachine learning classification algorithms (classifiers) for prediction of treatment response...
(A) Forest plot with 95% confidence intervals of novel signature classifiers. The forest plot is ord...
Motivation: The development of in silico models to predict chemical carcinogenesis from molecular st...
<p>Machine-learning tools were combined using balanced voting to generate meta-classifiers. The best...
<p>The results obtained with classifiers trained on the Wright et al. data using 20 classifier genes...
<p>The effect of training data set and classifier gene selection was assessed on a previously unseen...
<p>Colored boxes (gray/green) depict different training data sets. Step 1- assessment of individual ...
<p>(The feature sets are labeled by their size, here 6 features, and then enumerated from 0 to N–1, ...
Microarray data are obtained from specific platforms and preprocessing using 24 different pipelines ...
The present article is devoted to experimental investigation of the performance of three machine lea...
The purpose of this report is to compare three different classifiers through supervised machine lear...
Motivation The development of in silico models to pre-dict chemical carcinogenesis from molecular st...
Abstract Background Selecting an appropriate classifier for a particular biological application pose...
<p>Accuracy, F-measure (F1 Score), precision, recall, correlation coefficient (C.C.), and area under...
<p>(*maximum no. of genes per base classifier in ensemble learning model; **evaluation results avera...
PurposeMachine learning classification algorithms (classifiers) for prediction of treatment response...
(A) Forest plot with 95% confidence intervals of novel signature classifiers. The forest plot is ord...
Motivation: The development of in silico models to predict chemical carcinogenesis from molecular st...
<p>Machine-learning tools were combined using balanced voting to generate meta-classifiers. The best...
<p>The results obtained with classifiers trained on the Wright et al. data using 20 classifier genes...
<p>The effect of training data set and classifier gene selection was assessed on a previously unseen...
<p>Colored boxes (gray/green) depict different training data sets. Step 1- assessment of individual ...
<p>(The feature sets are labeled by their size, here 6 features, and then enumerated from 0 to N–1, ...
Microarray data are obtained from specific platforms and preprocessing using 24 different pipelines ...
The present article is devoted to experimental investigation of the performance of three machine lea...
The purpose of this report is to compare three different classifiers through supervised machine lear...
Motivation The development of in silico models to pre-dict chemical carcinogenesis from molecular st...
Abstract Background Selecting an appropriate classifier for a particular biological application pose...
<p>Accuracy, F-measure (F1 Score), precision, recall, correlation coefficient (C.C.), and area under...
<p>(*maximum no. of genes per base classifier in ensemble learning model; **evaluation results avera...
PurposeMachine learning classification algorithms (classifiers) for prediction of treatment response...
(A) Forest plot with 95% confidence intervals of novel signature classifiers. The forest plot is ord...
Motivation: The development of in silico models to predict chemical carcinogenesis from molecular st...