Finding Frequent Patterns in Biological Networks

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Frequent patterns help to discover the structural behavior of the complex biological network. The networks which show same global structure may have different local structure. The patterns are the actual fingerprints of the network. The pattern frequency and its significance carries very important functionality to classify and cluster the biological network. The sheer number of patterns get generated while traversing the network. Counting these patterns and determining their frequencies in the large biological network is very challenging and computationally expensive task. Previously proposed methods are bound by the size of patterns and networks size. By using approximation method like sampling, we proposed an algorithm. The results show the proposed algorithm is faster than existing algorithms. Also, the error rate is minimum, which makes the proposed method more reliable.

     

     

  • Keywords


    MCMC; Network motifs; Sampling algorithms.

  • References


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Article ID: 20482
 
DOI: 10.14419/ijet.v7i4.6.20482




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