Conclusion and FutureWorks

In this book, a brief history and survey of the state of the art in the field of data integration, modeling and pattern identification with respect to pathway mining and presented a number of potential and challenging directions in which it could be extended. The prospects of the application of bioinformatics to pathway analysis have attracted increasing attentions in the past two decades due to the importance of pathway in understanding complex regulatory networks of biology systems. In recent years, these approaches and techniques have been showing their maturity and consolidation. A lot of specialized or general-purpose algorithms and tools have been proposed and used to realistic pathway data analysis, in many cases generating interesting and valuable biology knowledge to biologist that can be further applied to enhance and prompt the relevant study of life science, such as disease diagnosis and drug design. However, it is necessary for us to recognize that some emerging challenges, such as new and complex systems biology and meta-genome and new types of regulatory factors, such as long ncRNA, give rise to new requirements to existing pathway data analysis. Any efforts to discover and construct pathwaysmust taken them into account. These give rise some a series of important issues that need to be investigated for improving the correctness and efficiency of pathway mining.