<p>(*maximum no. of genes per base classifier in ensemble learning model; **evaluation results averaged over feature subsets using different numbers of genes).</p
Gene expression based cancer classification using classifier ensembles is the main focus of this wor...
Abstract—The combination of multiple classifiers using ensem-ble methods is increasingly important f...
<p>(<b>a</b>) Clustered heatmaps of the predicted genes (columns) reveal that two best-performer tea...
<p>(*maximum no. of genes per base classifier in ensemble learning model; **evaluation results avera...
<p>Comparison of prediction accuracy on four binary classification datasets by varying the number of...
<p>Comparison of prediction accuracy on four multiclass classification datasets by varying the numbe...
<p>Histogram of text mining scores for randomly chosen gene identifier subsets compared to scores ac...
In genetic data modeling, the use of a limited number of samples for modeling and predicting, especi...
Motivation: Class predicting with gene expression is widely used to generate diagnostic and/or progn...
<p>List of genes that were chosen by at least two different selection methods among the 30 features ...
Motivation: Class predicting with gene expression is widely used to generate diagnostic and/or progn...
<p>Histogram of text mining scores for randomly chosen gene identifier subsets, compared to scores a...
With the advent of inexpensive microarray technology, biologists have become increasingly reliant on...
Abstract: Statistical methods for analyzing large-scale biomolecular data are commonplace in computa...
Abstract Background The information from different data sets experimented under different conditions...
Gene expression based cancer classification using classifier ensembles is the main focus of this wor...
Abstract—The combination of multiple classifiers using ensem-ble methods is increasingly important f...
<p>(<b>a</b>) Clustered heatmaps of the predicted genes (columns) reveal that two best-performer tea...
<p>(*maximum no. of genes per base classifier in ensemble learning model; **evaluation results avera...
<p>Comparison of prediction accuracy on four binary classification datasets by varying the number of...
<p>Comparison of prediction accuracy on four multiclass classification datasets by varying the numbe...
<p>Histogram of text mining scores for randomly chosen gene identifier subsets compared to scores ac...
In genetic data modeling, the use of a limited number of samples for modeling and predicting, especi...
Motivation: Class predicting with gene expression is widely used to generate diagnostic and/or progn...
<p>List of genes that were chosen by at least two different selection methods among the 30 features ...
Motivation: Class predicting with gene expression is widely used to generate diagnostic and/or progn...
<p>Histogram of text mining scores for randomly chosen gene identifier subsets, compared to scores a...
With the advent of inexpensive microarray technology, biologists have become increasingly reliant on...
Abstract: Statistical methods for analyzing large-scale biomolecular data are commonplace in computa...
Abstract Background The information from different data sets experimented under different conditions...
Gene expression based cancer classification using classifier ensembles is the main focus of this wor...
Abstract—The combination of multiple classifiers using ensem-ble methods is increasingly important f...
<p>(<b>a</b>) Clustered heatmaps of the predicted genes (columns) reveal that two best-performer tea...
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