<p>(<b>a</b>) Clustered heatmaps of the predicted genes (columns) reveal that two best-performer teams predicted substantially similar gene expression values, though different methods were employed. Results for the 60 minute time-point are shown. (<b>b</b>) The benefits of combining the predictions of multiple teams into a consensus prediction are illustrated by the rank sum prediction (triangles). Some rank sum predictions score higher than the best-performer, depending on the teams that are included. The highest score is achieved by a combination of the predictions of the best four teams.</p
We address the problem of learning a predictive model for growth inhibition from the NCI DTP human t...
A large number and variety of genome-wide genomics and proteomics datasets are now available for mod...
Abstract Background Machine learning models (classifiers) for classifying genes to biological proces...
A wealth of computational methods has been developed to address problems in systems biology, such as...
BACKGROUND: To predict gene expressions is an important endeavour within computational systems biolo...
To predict gene expressions is an important endeavour within computational systems biology. It can b...
Background: To predict gene expressions is an important endeavour within computational systems biolo...
<p><i>P</i>-values at each time-point and a summary <i>p</i>-value (geometric mean) are indicated.</...
(A) Comparison of predictive performance for each gene (R2) between each pair of populations. Predic...
<p>A) Gene expression values. B) Gene expression values predicted from miRNA expression. C) Correlat...
In "Random Promoter DREAM Challenge 2022: Predicting gene expression using millions of random promot...
<p>Shown are the 8 genes for which the KNN algorithm that integrated genomic features resulted in th...
<p>The following steps are taken: 1) The methods (Bayes Factor, ROKU-SPM and Decision F = Decision f...
Abstract Background The information from different data sets experimented under different conditions...
MOTIVATION: Network-based gene function inference methods have proliferated in recent years, but mea...
We address the problem of learning a predictive model for growth inhibition from the NCI DTP human t...
A large number and variety of genome-wide genomics and proteomics datasets are now available for mod...
Abstract Background Machine learning models (classifiers) for classifying genes to biological proces...
A wealth of computational methods has been developed to address problems in systems biology, such as...
BACKGROUND: To predict gene expressions is an important endeavour within computational systems biolo...
To predict gene expressions is an important endeavour within computational systems biology. It can b...
Background: To predict gene expressions is an important endeavour within computational systems biolo...
<p><i>P</i>-values at each time-point and a summary <i>p</i>-value (geometric mean) are indicated.</...
(A) Comparison of predictive performance for each gene (R2) between each pair of populations. Predic...
<p>A) Gene expression values. B) Gene expression values predicted from miRNA expression. C) Correlat...
In "Random Promoter DREAM Challenge 2022: Predicting gene expression using millions of random promot...
<p>Shown are the 8 genes for which the KNN algorithm that integrated genomic features resulted in th...
<p>The following steps are taken: 1) The methods (Bayes Factor, ROKU-SPM and Decision F = Decision f...
Abstract Background The information from different data sets experimented under different conditions...
MOTIVATION: Network-based gene function inference methods have proliferated in recent years, but mea...
We address the problem of learning a predictive model for growth inhibition from the NCI DTP human t...
A large number and variety of genome-wide genomics and proteomics datasets are now available for mod...
Abstract Background Machine learning models (classifiers) for classifying genes to biological proces...