MAPK signal transduction pathways are widespread in eukaryotic cells, and changes in the external environment could activate these pathways. This study makes use of yeast gene microarray experimental data to predict the MAPK signal transduction pathway order. Two sets of experimental data are used, a group containing the signal transduction pathway data of 56 experiments, the other group includes 300 different mutations and chemical treatment experiments. The calculation is based on computing the absolute sum of Pearson correlation coefficient (PCC) between a mRNA and the nearby mRNA expression. The total score of a pathway is given by the sum of the individual pair along the pathway. Pathway has the maximum score is the predicted pathway. It is found that the real MAPK pathway fall on the first 15% among all possible pathways. Furthermore, we integrate the Function-Function Correlation (FFC) data, to improve the pathway forecast ranking. After joining the FFC data a few results get improved slightly. Finally, we use this method in the protein complex systems, forecasting the sub-units order in assembling a protein complex. A web based service is set up which offer the following features: (i) forecast the most likely MAPK signal transduction pathway for yeast, according to microarray data, (ii) compute the PCC for a pair of mRNAs, (iii) predicting the assembling order for a protein complex, and (iv) two Delphi programs for computing the FFC values for a pair of proteins.
