The microarray datasets used for classification.

<p>The microarray datasets used for classification.</p

The accuracy of <i>sumdiff/mul/sign-k-</i>NN for the top % genes compared with the <i>k-</i>NN accuracy for each of the nine datasets.

<p>The accuracy of <i>sumdiff/mul/sign-k-</i>NN for the top % genes compared with the <i>k-</i>NN accuracy for each of the nine datasets.</p

The accuracy of <i>sumdiff/mul/sign-</i>SVM for the top % genes compared with the SVM accuracy for each of the nine datasets.

<p>The accuracy of <i>sumdiff/mul/sign-</i>SVM for the top % genes compared with the SVM accuracy for each of the nine datasets.</p

The accuracy of <i>sumdiff/mul/sign-</i>NB for the top % genes compared with the NB accuracy for each of the nine datasets.

<p>The accuracy of <i>sumdiff/mul/sign-</i>NB for the top % genes compared with the NB accuracy for each of the nine datasets.</p

KEGG pathways related to the top 15 doublets for the CNS dataset.

<p>KEGG pathways related to the top 15 doublets for the CNS dataset.</p

LOOCV accuracy of the classifiers for the binary class expression datasets.

<p>*Results obtained in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014305#pone.0014305-Tan1" target="_blank">[6]</a></p>†<p>Results from taking the top 4% of genes for making unique doublets.</p

The accuracy of <i>sumdiff/mul/sign-PAM for the top </i>% genes compared with the PAM accuracy for each of the nine datasets.

<p>The accuracy of <i>sumdiff/mul/sign-PAM for the top </i>% genes compared with the PAM accuracy for each of the nine datasets.</p

Additional file 1 of Prioritizing biological pathways by recognizing context in time-series gene expression data

Criteria of gene selection and selected gene set. It provides how to select the gene sets to find relevant pathways from each dataset and the result of selection mentioned in section Data processing. (XLS 49 kb

Additional file 1 of CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments

Table S1. Performance comparison of CLIP-GENE (excluding and including network) while analyzing Gata3, Setd2, and Barx2 knockout data. Table S2. Performance comparison of CLIP-GENE (applied network and RegNetwork) while analyzing Gata3, Setd2, and Barx2 knockout data. Table S3. Performance comparison of CLIP-GENE (no-context, best context, worst context, combination of context) while analyzing Gata3, Setd2, and Barx2 knockout data. (DOCX 20.4 kb

The number of published papers related to the keyword ‘cancer’ since 2010.

<p>More than 100,000 papers have been published every year.</p