PATHOME-drug overview
  • The translation of high-throughput gene expression data into biologically meaningful knowledge remains a bottleneck. Previously, we developed a novel computational algorithm, PATHOME (pathway and transcriptome), for detecting differentially expressed biological pathways. This algorithm employs straightforward statistical tests to evaluate the significance of differential expression patterns along subpathways.
  • In our previous publication (Oncogene (2014) 33, 4941–4951), we applied the algorithm to gene expression data sets of gastric cancer (GC), identifying HNF4α-WNT5A regulation in the cross-talk between the AMPK metabolic pathway and the WNT signaling pathway. Also, we identified WNT5A as a novel potential therapeutic target for GC.
  • For providing our algorithm to the biomedical society, we implemented the algorithm into a web server, called PATHOME-drug.

Figure 1. Extraction of network from big data repository and its visualization.

Note: This section contains some parts of our previous publication (Oncogene (2014), 33, 4941-4951) under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
Key features
  • Big-data driven network inference
  • Web-based visualization of results
  • Drug enrichment analysis
Sample output
  • Sample input dataset (download)
    • The sample dataset is the gastric cancer dataset (GEO accession: GSE15081) with the two groups: c1 (peritoneal relapse, 38 samples) vs. c2 (relapse-free, 18 samples).
    • Since his dataset is log2-transformed dataset, there is no need to select '2. Is the dataset log2-scaled?' checkbox when submitting a job using this dataset.
  • Sample output
Contact information

  • Web server bug report
  • If there is a trouble during the preparation of dataset or the analysis, please let us know to resolve the problem via (Sungyoung Lee, the current maintainer).

  • Contacts
  • PATHOME-drug version 1.0
    All rights reserved to BIBS laboratory, Seoul National University, Korea.