A PAthway and Network DiscOveRy Approach based on common biological evidence.

Pandora logoMany biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information. Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein-protein interaction, genetic interaction, domain-domain interaction, and semantic similarity of GO terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to two public pathway databases (KEGG and Reactome), we observed that our approach achieves a positive prediction value of 12.8% and improves on other predictive approaches.

This study allows us to reconstruct biological pathways and delineates the cellular machinery in a systematic view.

The software is Open Source and is released under the GNU General Public License.

If you use Pandora in your work, please cite the paper list above, published in Bioinformatics

Source Code:
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(4 MB)
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Contact Information

Mr. Francis Ouellette
Francis Ouellette, Associate Director and Senior Scientist, Informatics and Bio-computing, Ontario Institute for Cancer Research (OICR)