Identification of gene network motifs for cancer disease diagnosis

All networks, including biological networks, computer networks, social networks and more can be represented as graphs, which include a number of small module such as subgraph, also called as network motifs. Network motifs are subgraph which recur themselves in a specific network or different networks. In biological networks, these network motifs plays very important role to identify diseases in human beings. In this paper we have developed a module to identify common network motifs types from cancer pathways and Signal Transduction Networks (STNs). It also identifies the topological behaviors o f cancer networks and STNs. In this study, we have implemented five motif algorithms such as Auto-Regulation Loop (ARL), Feed Backward Loop (FBL), Feed Forward Loop (FFL), Single-Input Motif (SIM) and Bi-fan. These algorithms gives correct results in terms of network motifs for human cancer and STNs. Finding network motifs by using online tool is limited to three nodes, but our proposed work provides facility to find network motifs upto any number of nodes. We applied five motif algorithms to human cancer networks and Signal Transduction Networks (STNs) which are collected from KEGG database as a result we got “Frequent Occurrences of Network Motifs (FONMs)”. These FONMs acts as a references for an oncologist in order to find type o f cancer in human beings.