<p>The image shows the similarity between the subtypes distribution for METABRIC discovery (MD) and validation (MD) sets, and ROCK test set (RS). The labels were assigned in the original data sets using the PAM50 method, and relabelled in this study with an ensemble learning using PAM50 and CM1 lists. The similarity is measured using the square root of the Jensen-Shannon divergence. Darker shades represent more similar distributions, while lighter shades refer to divergent patterns. The diagonal shows the darkest color as each data set is the closest to itself. According to this image, labels assigned using an ensemble learning with CM1 and PAM50 lists are highly similar, and both exhibit lower levels of agreement with the original labels a...
a: Dot chart of MKL model mean accuracy and standard deviation for each similarity measure and metho...
<p>All plots have the same construction. The x-axis shows the number of top pairs that are considere...
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem o...
<p>The bars represent the number of samples in each breast cancer subtype. In the first row, the lab...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with PR positive and n...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with ER positive and n...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with <i>HER2</i> ampli...
<p>Rows contain labels assigned by the majority of classifiers trained with the CM1 list, while colu...
<p>Rows contain labels assigned by the majority of classifiers trained with the PAM50 list, while co...
<p>Rows entitled <i>Among classifiers</i> indicate agreement of classifiers alone, not considering t...
<p>This contains the agreement between the original and predicted labels of samples in the discovery...
<p>Rows contain the labels assigned by the majority of classifiers trained with the CM1 list, while ...
<p>Values are given as <i>average</i> ± <i>std. deviation</i>. CV- Cramer’s V; AS- Average Sensitivi...
<p>The MST-4NN graphs of samples from the METABRIC (<b>a</b>) training and (<b>b</b>) validation set...
BACKGROUND: Multi-gene lists and single sample predictor models have been currently used to reduce t...
a: Dot chart of MKL model mean accuracy and standard deviation for each similarity measure and metho...
<p>All plots have the same construction. The x-axis shows the number of top pairs that are considere...
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem o...
<p>The bars represent the number of samples in each breast cancer subtype. In the first row, the lab...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with PR positive and n...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with ER positive and n...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with <i>HER2</i> ampli...
<p>Rows contain labels assigned by the majority of classifiers trained with the CM1 list, while colu...
<p>Rows contain labels assigned by the majority of classifiers trained with the PAM50 list, while co...
<p>Rows entitled <i>Among classifiers</i> indicate agreement of classifiers alone, not considering t...
<p>This contains the agreement between the original and predicted labels of samples in the discovery...
<p>Rows contain the labels assigned by the majority of classifiers trained with the CM1 list, while ...
<p>Values are given as <i>average</i> ± <i>std. deviation</i>. CV- Cramer’s V; AS- Average Sensitivi...
<p>The MST-4NN graphs of samples from the METABRIC (<b>a</b>) training and (<b>b</b>) validation set...
BACKGROUND: Multi-gene lists and single sample predictor models have been currently used to reduce t...
a: Dot chart of MKL model mean accuracy and standard deviation for each similarity measure and metho...
<p>All plots have the same construction. The x-axis shows the number of top pairs that are considere...
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem o...