Abstract
In protein Interaction Networks, counting subgraph is a tedious task. From the list of non induced occurrence of the subgraph, motif topology calculated by using Combi Motif and Slider techniques. But, this approach was taken more time to execute. To reduce the execution time, the minimum weight value between the nodes, the Minimum spanning tree concept proposed. Prim’s method implemented with the greedy technique (as Kruskal’s algorithm) to calculate the minimum path between the nodes in the Protein interaction network. This technique uses to compare the similarity of the minimum spanning tree approach. Initially, this algorithm has discovered the path then calculated the weight matrix and found the minimum weight value. From the computational experiments, the proposed approach of MST providing better results in terms of time consumption and accuracy to count the motif pattern in the network of the interacted proteins.
Highlights
Proteins are a combination of amino acids, to carry out their function processes with other molecules, these biochemical activities in living cells are called Protein interactions [1]
Graphs are categorized into Induced and NonInduced - Induced subgraph denotes, along with any edges, vertices and the endpoints are both in the subset, it‟s determined by vertices selected
The contribution of this paper is to identify and count the similar motif pattern in the protein interaction network
Summary
Proteins are a combination of amino acids, to carry out their function processes with other molecules, these biochemical activities in living cells are called Protein interactions [1]. Protein interaction prediction is a challenging task in bioinformatics. PPI predicted by various relevant biological information such as functions of proteins, protein sequences, gene expression, PIN, Gene interaction /Networks at the whole molecular level based on Amino acid sequences, structure information, physicochemical properties, etc.,[2.] Two classification aspects are addressed, first based on various attributed and features, second to predict whether the proteins have interacted or not interacted These aspects depend upon the identification of the various sources of data, the information contains Gene ontology, Details of phylogenetic approach, and Synthesis of the gene, Genomic circumstances, Gene and Protein sequence conservation depends on the Protein interactions [3, 4]. To reduce the cost and time, computational results are better than experimental methods, some computational methods of predicting PPI are 3D structural information through algorithm in human and yeast, Probabilistic decision tree with high throughput datasets to characterize the co-complex protein pair and [7], Incomplete
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