Protein Networks Identification Based On The Degree Of Closely Associated Nodes Measure

How to identify effective protein functional modules from protein interaction network has a very important biological significance. Therefore, we made some improvements point to the drawbacks of conventional clustering algorithms and proposed a new adaptive clustering algorithm. Using bioinformatics to find the relation between biological data, which can help us to predict disease genes, is very promising and also challenging work. We applied CAD algorithm to human protein network, and finally accomplished the prediction work of breast cancer’s disease-related genes. The main contents are as follows:(1) Modularity is the standard to measure the division of complex network modules, however, the most widely used NG (Newman-Girvan) modularity has the limit of resolution. To overcome this defect, we introduced density modularity. To get better module structure, we propose a Closely Associated Degree (CAD) algorithm to discover protein functional module which continuously improve density modularity of PPI network. CAD first analyze the associated degree of protein node, then join it into the maximal associated degree module. When all modular structure are stable, CAD merges the pair of module that can bring the maximum increment of density modularity. This process is continually repeated that make density modularity to grow rapidly. Experimental results show that the CAD algorithm can effectively and accurately identify protein functional modules with biological significance in large-scale PPI network.(2) It has been known that disease is produced by the complex interactions between biological molecules (especially protein molecules). Thus, using bioinformatics to find the relation between biological data, which can help us to predict disease genes, is very promising and also challenging work. As we applied the CAD algorithm to the yeast protein networks, experimental results show that the CAD algorithm can effectively and accurately identify protein functional modules with biological significance in large-scale PPI network, therefore, the predicted disease genes which get by CAD algorithm when applied the algorithm to the human protein networks should also have a certain accuracy. This article applied CAD algorithm to human protein network to predict breast cancer’s disease-related genes, ultimately, we get20predicted genes.