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The Scientific World Journal
Volume 2012, Article ID 315797, 14 pages
http://dx.doi.org/10.1100/2012/315797
Research Article

Pathway Detection from Protein Interaction Networks and Gene Expression Data Using Color-Coding Methods and A* Search Algorithms

1Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan
2Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 300, Taiwan

Received 9 September 2011; Accepted 17 October 2011

Academic Editor: Shanker Kalyana-Sundaram

Copyright © 2012 Cheng-Yu Yeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

We compared our method to the previous stat-of-art methods and reconstruct three known signal pathways in KEGG database: a pheromone response signaling pathway, a filamentous growth pathway and cell wall integrity pathway. The proteins in three pathways detected by previous and our methods are shown in Table S1.

In order to deal with missing values in microarray data, we evaluated K-nearestneighbors (KNN) algorithm to determine the precise expression values. First, we deleted 1,750 original values at random one by one to create test data sets and estimated the missing value to compare with the original value. The accuracy of estimation values are calculated by Root Mean Squared Error (RMSE) with different numbers of neighbors which are shown in Figure S1.

In the experiments, we tested our method to identify prostate cancer-related networks from EGFR to BCL2 with length 15 and the results are shown in Figure S2 and S3. Table S2 denotes proteins in those networks which are confirmed by biological evidences from the public literature and databases.

  1. Supplementary Materials