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Detecting Community Structures in Patients with Peripheral Nervous System Disorders

Hosseinioun, Morteza | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 53056 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Hemmatyar, Ali Mohammad Afshin; Movaghar, Ali
  7. Abstract:
  8. Nowadays, many biological systems are modeled by bipartite networks. So, using network's concepts and techniques such as community detection may lead us to more accurate outcomes.In this thesis, we have presented an algorithm for detection of communities in a network of patients with Peripheral Nervous System (PNS) issues. The bipartite network of the patients is formed based on the dataset which is collected with cooperation of a Spinal Specialty Clinic. In our algorithm, called MRComSim, three different methods are used to project the bipartite graph to unipartite graph. Then the projected unipartite graph of each method is used for community detection. The output result of our algorithm is in agreement up to 85.90% with the Diagnosis of Doctor (DofD)
  9. Keywords:
  10. Bipartite Networks ; Community Detection ; Peripheral Nerve Injury ; Nervous System

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