Additional File 2:Supplementary Tables.(PDF 134 KB).pdf

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Preview of Additional file 2:Supplementary tables.(PDF 134 KB)
🔗 Source: static-content.springer.com
📊 Size: 133 KB
👤 Author: Ka Yee Yeung
⬇️ Downloads: 91

Summary

The construction of regulatory networks from time-series expression data is enhanced by integrating external biological knowledge. Various methods, including iBMA-prior, iBMA-shortlist, iBMA-size, and iBMA-noprior, are assessed based on their performance in inferring regulatory relationships. The results show that iBMA-prior and iBMA-size outperform other methods, with higher true positive rates (TPR) and observed-to-expected (O/E) ratios. The assessment is based on the time-series yeast gene expression data and the Yeastract database. The methods are also evaluated at different posterior probability thresholds, and the results indicate that iBMA-prior and iBMA-size maintain their superior performance across various thresholds. Additionally, the estimated regression coefficients and posterior probabilities for each external data type are provided, highlighting the importance of incorporating external biological knowledge in the construction of regulatory networks. The area under the precision-recall curve (PRC) is also calculated for iBMA-based methods, further demonstrating their effectiveness in inferring regulatory relationships.

Description

The construction of regulatory networks from time-series expression data is enhanced by integrating external biological knowledge.

Technical Information

  • File Format: PDF
  • File Size: 133 KB
  • Pages: 5
  • Language: EN
  • Author: Ka Yee Yeung
  • Total Downloads: 91
  • Last Updated: 1 month ago

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