<p>For each experiment, the optimal combination of two thresholds was obtained using the approach mentioned above and was applied to an independent test using unlabeled samples. Bold font indicates the superior performer.</p><p>TSVM: <i>P</i> (the ratio of two class labels).</p><p>SVM: PolyKernel –C 250007–E 1.0, The complexity parameter C (1.0), epsilon (1.0E−12), filterType (Normalized training data).</p><p>Naïve Bayesian: No parameters.</p><p>Random Forest: numTrees (10), seed (1).</p
Distributions of multi-class macro F1 score for prediction of growth conditions from mRNA or protein...
Abstract Background Machine learning models (classifiers) for classifying genes to biological proces...
(A) Comparison of predictive performance for each gene (R2) between each pair of populations. Predic...
The completion of the human genome and the advancement of high-throughput technologies have enable t...
The completion of the human genome and the advancement of high-throughput technologies have enable t...
Abstract: In phenotype prediction the physical characteristics of an organism are predicted from kno...
Abstract: In phenotype prediction the physical characteristics of an organism are predicted from kno...
In this work, we study the performance of different algorithms for learning gene networks from data...
<p>Each method is evaluated by the number of genes observed to be consistently expressed across samp...
Availability of high dimensional biological datasets such as from gene expression, proteomic, and me...
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical m...
<p>Shown are the prediction performance of the proposed hybrid method using both gene sets and singl...
For feature selection using coefficient of variation (CV), the filtering thresholds from left to rig...
<p>(*maximum no. of genes per base classifier in ensemble learning model; **evaluation results avera...
<p>A. The histogram shows the score distribution of the instances in the positive bags and the negat...
Distributions of multi-class macro F1 score for prediction of growth conditions from mRNA or protein...
Abstract Background Machine learning models (classifiers) for classifying genes to biological proces...
(A) Comparison of predictive performance for each gene (R2) between each pair of populations. Predic...
The completion of the human genome and the advancement of high-throughput technologies have enable t...
The completion of the human genome and the advancement of high-throughput technologies have enable t...
Abstract: In phenotype prediction the physical characteristics of an organism are predicted from kno...
Abstract: In phenotype prediction the physical characteristics of an organism are predicted from kno...
In this work, we study the performance of different algorithms for learning gene networks from data...
<p>Each method is evaluated by the number of genes observed to be consistently expressed across samp...
Availability of high dimensional biological datasets such as from gene expression, proteomic, and me...
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical m...
<p>Shown are the prediction performance of the proposed hybrid method using both gene sets and singl...
For feature selection using coefficient of variation (CV), the filtering thresholds from left to rig...
<p>(*maximum no. of genes per base classifier in ensemble learning model; **evaluation results avera...
<p>A. The histogram shows the score distribution of the instances in the positive bags and the negat...
Distributions of multi-class macro F1 score for prediction of growth conditions from mRNA or protein...
Abstract Background Machine learning models (classifiers) for classifying genes to biological proces...
(A) Comparison of predictive performance for each gene (R2) between each pair of populations. Predic...