Abstract
Systems biology is a research field where experimental and computational approaches are used in combination. Core experimental data tends to be multidimensional and large-scale. Consequently, the requirements for data processing are sensitive and intricate. Multiple data whose nature is not the same are difficult to deal with computationally, and dealing with a large amount of data that may not be of high quality is also difficult. We dealt with such complex data using a data analysis pipeline and show that transcription networks are a promising theme of systems biology.