A) Clustering performance (ARI, y−axis, the higher the better) in the latent space of each model (x−axis) compared to the true cancer type on the TCGA dataset. Each box represents the distribution of scores obtained for different hyperparameter settings within a specific VAE model. The middle line corresponds to the mean, while the edges of the box represent the first and third quartiles. B) As in A) but for the supervised task of predicting overall survival. Performance is measured by the AIC (y−axis, the lower the better) and the dashed red line indicates the baseline model performance using the covariates only (i.e., age, gender and cancer types).</p
Using RNA sequence data for predicting patient properties is fairly common by now. In this paper, Va...
A listing of the hyperparameters that were held constant throughout the study. The values were set a...
We developed a model that performs unsupervised clustering of survival times in a jointsurvival-lon...
Scatter plot for the clustering performance measured in ARI (y−axis, the higher the better) and surv...
A) Clustering performance (ARI, y−axis, the higher the better) in the latent space of each model (x−...
Each boxplot shows the clustering performance (ARI, x−axis) of fixing a hyperparameter while varying...
Each boxplot shows the clustering performance (ARI, x−axis) of fixing a hyperparameter while varying...
Plotting the 90th percentile (i.e., excluding the highest 10%) of the of the validation loss (y−axis...
Cancer has been known as a deadly and complex disease to tackle. By applying machine learning algori...
Scatter plot for the 90th percentile of the validation loss of different hyperparameters configurati...
Scatter plot for the 85th percentile of the validation loss of different hyperparameters configurati...
The figure shows the WSEPIN score on the y−axis, while the bars are colored after the different late...
<p>Two models were compared T2 and T2Tex using the metrics: TPR = Sensitivity, SPC = Specificity, PP...
Scatter plot for the 90th percentile of the validation loss of different hyperparameters configurati...
<p>(<b>A</b>) Performance of the subjects (black line) and of the complete model (gray line) as a fu...
Using RNA sequence data for predicting patient properties is fairly common by now. In this paper, Va...
A listing of the hyperparameters that were held constant throughout the study. The values were set a...
We developed a model that performs unsupervised clustering of survival times in a jointsurvival-lon...
Scatter plot for the clustering performance measured in ARI (y−axis, the higher the better) and surv...
A) Clustering performance (ARI, y−axis, the higher the better) in the latent space of each model (x−...
Each boxplot shows the clustering performance (ARI, x−axis) of fixing a hyperparameter while varying...
Each boxplot shows the clustering performance (ARI, x−axis) of fixing a hyperparameter while varying...
Plotting the 90th percentile (i.e., excluding the highest 10%) of the of the validation loss (y−axis...
Cancer has been known as a deadly and complex disease to tackle. By applying machine learning algori...
Scatter plot for the 90th percentile of the validation loss of different hyperparameters configurati...
Scatter plot for the 85th percentile of the validation loss of different hyperparameters configurati...
The figure shows the WSEPIN score on the y−axis, while the bars are colored after the different late...
<p>Two models were compared T2 and T2Tex using the metrics: TPR = Sensitivity, SPC = Specificity, PP...
Scatter plot for the 90th percentile of the validation loss of different hyperparameters configurati...
<p>(<b>A</b>) Performance of the subjects (black line) and of the complete model (gray line) as a fu...
Using RNA sequence data for predicting patient properties is fairly common by now. In this paper, Va...
A listing of the hyperparameters that were held constant throughout the study. The values were set a...
We developed a model that performs unsupervised clustering of survival times in a jointsurvival-lon...