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Open Access Highly Accessed Methodology article

A simple knowledge-based mining method for exploring hidden key molecules in a human biomolecular network

Shingo Tsuji12, Sigeo Ihara1 and Hiroyuki Aburatani1*

Author Affiliations

1 Genome Science Division, Research Center for Advanced Science and Technology (RCAST), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan

2 Komaba Open Laboratory, The University of Tokyo, Tokyo, Japan

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BMC Systems Biology 2012, 6:124  doi:10.1186/1752-0509-6-124

Published: 15 September 2012

Additional files

Additional file 1:

The input gene list of the ErbB pathway and GBM analysis. Two input gene lists were used for this study. One contains the ligands and transcription factors from the ErbB pathway, and the other contains the mutated genes in GBM with the frequencies as the weights. (http://www.microsoft.com/download/en/details.aspx?id=10 webcite).

Format: XLSX Size: 93KB Download file or display content in a new window

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Additional file 2:

The collection of results for the Pathway Interaction Database analysis. The index.html file contains the links to the Pathway Interaction Database results for the various input genes. The input genes consist of the results of NetHiKe and Hubba (the top 30 genes of each). (Mini-websites, browse the index.html.

Format: ZIP Size: 93KB Download file or display content in a new window

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Additional file 3:

Degree, P-value, and nlBC for the ErbB pathway analysis data. Plots of the degrees, p-values, and nlBC values of genes with P<0.05 in the results of the ErbB pathway analysis (A-C) and boxplots of the nlBC values (D and E). A) Plot of the node degrees in the background network vs. nlBC. B) Degree vs. simulated p-values. C) nlBC vs. p-values. D) Boxplot visualization of the genes in Table 1. The boxes are the nlBC values generated from randomly selected genes to calculate the simulated p-values, and the yellow dots denotes the actual nlBC value that was calculated based on the input genes (listed in the Additional file 1). The simulated p-values, listed in the Table 1, are plotted as the red line associated with the right axis. E) The nlBC values that were generated by a leave-one-out method using the input genes, and the actual nlBC values as the yellow dots. The plot D and E have the same Y-axis scale (left) and the gene order in X-axis.

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Additional file 4:

Comparison lists of the top 30 genes on NetHiKe and Hubba. The lists of the top 30 genes generated by NetHiKe and Hubba with the same input data. In the Hubba analysis, six different methods were used. The six-digit number indicates the Pathway Commons ID, as the molecules do not have general gene names.

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Additional file 5:

The NetHiKe results of the weighted NRG2. The sheet named “NRG2_weighted_2.0” is the NetHiKe result of the input with the NRG2 weight set to 2.0, and “NRG2_weighted_20.0” is the result with the NRG2 weight set to 20.0. The genes with p-values less than 0.05 are listed. (http://www.microsoft.com/download/en/details.aspx?id=10 webcite).

Format: XLSX Size: 48KB Download file

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Additional file 6:

The GBM network and key molecules inferred by NetHiKe. The extracted network made by genes mutated in GBM. The blue-bordered nodes are the input nodes, and the p-values are shown by the depth of the red color.

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Additional file 7:

The comparison of the GBM analysis results between NetHiKe and Hubba. The sheet named “NetHiKe and Hubba results” contains the top 16 genes that were p<0.01 in the NetHiKe analysis and the same number of top-ranked genes from the various Hubba methods. There are no overlapping genes between the NetHiKe results and the Hubba results. However, there are several overlapping genes among the various Hubba methods. The second sheet, named “GBM_expression_1.2_ov40p,” contains the downloaded data from the TCA website to clarify the differentially expressed genes in the GBM analysis. (http://www.microsoft.com/download/en/details.aspx?id=10 webcite).

Format: XLSX Size: 265KB Download file

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Additional file 8:

Two different degree distributions depend on the edge selection. Log-log degree distribution for the network constructed from the whole Pathway Commons data (A) and the selected edges (B).

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