The pathogenesis and development of complex disease usually involve the dysfunction or dysregulation of numerous of genes and biological processes.Deciphering the biochemical pathways underlying the diseases is critical to understand their molecular mechanisms.Various pathway analysis tools have been developed to find the pathways significantly enriched in a given set of genes and to identify the correlation between pathways.However,existing methods usually treat all the genes in a pathway equally,ignoring the different role that genes may play in the pathway.With the continuous development of high-throughput analyzing technologies such as microarray and next generation sequencing in recent years,huge amount of information on genes related to a large number of diseases and on gene-gene interactions have been accumulated,which have been utilized to built more powerful bioinformatics tools,including those for pathway analysis.In this study,we designed a new weighted-based method to analyze pathway crosstalk.First,the candidate gene set and protein-protein interaction (PPI) network was collected from various sources.Then,for each pathway in the pathway database,each gene was assigned a weight according to available gene function information.After the gene enrichment analysis,pathways ranking on the top of the list were selected to build the pathway crosstalk.Two pathways were defined to be crosstalk with each other when there were a given number of overlapping genes between them.Through a series of evaluation,we found that the new method was more accurate than available approaches;and its results had lower redundancy and were more close to the real biological process.When testing on gene sets related to nicotine dependence,smoking initial and smoking cessation,we identified 89 pathway pairs (edges) from 74 pathways,31 pathway pairs (edges) from 57 pathways and 4 pathway pairs (edges) from 77 pathways,respectively.Further evaluation indicated that the results were biologically reliable and reproducible.