As the development of the biological technique, mountains of biological data appeared. It is an important goal to find out the answers to the concerning questions from the biological data. As the success of the whole genome sequencing, genome, protome, interactome become the hotspots of bioinformatics. The one to one analyses are not adequate for the data scale and biological questions. Methods of local and global analysis of network appeared. We studied different biological network from set analysis (local characteristic of network) and global network view and solved biological questions. microRNAs (miRNAs) are non-coding RNAs that play key roles in almost every critical process. It is important to understand their functions and characteristics. We studied the associations between recombination rates of miRNAs and their tissue specificity, expression levels and related disease numbers, and found out that they were all significantly related. We also used miRNA set enrichment analysis to found the differentiation expressed miRNAs in schizophrenia. We then reconstructed miRNA co-expression network and gene co-expression network, compared the two networks by their toopological characteristics and the miRNA-gene regulation, and found out a miRNA module that is associated with schizophrenia. It is an important method to reconstruct gene interaction network from gene expression profiles and carried on further studies based on this network. We reconstructed a brain-related gene interaction network from different gene expression profiles, and found the differentiated genes in the incipient, moderate, severe stages of Alzheimer's disease respectively. We also combined protein-protein interaction network to improve the accurary, and got two differentiated biological function clusters that consistented with the processes of the Alzheimer’s disease, neuron functions and Ca2+<上标!> homeostasis changes. Metabolic network is a network that includes the relations of genes, proteins and reactions. We used flux balance anlaysis and in-silico gene deletion in the metabolic networks in model organisms, and grouped the genes into important genes and non-important ones. We mapped these genes to human genome, and compared the recombination rates of the orthologous genes of two groups. We found out that the important genes had lower recombination rates than the non-important ones and more conservative. As a whole, we analyzed the local and the global characteristics ...
修改评论