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Innovative Pathway Clustering via Graph Mining Approach by Mahmud Shahriar Hossain and Team

Explore the design pipeline for clustering pathways using a graph mining approach. Learn about dataset preprocessing, frequent subgraph discovery, and mining pathway relations. Discover pathway properties and the benefits of pruning edges in subgraph discovery. Save time and mining attempts with nearest neighbors cover tree and brute-force methods. Uncover bidirectional search and storytelling through pathway relations. Find answers to key questions in pathway clustering.

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Innovative Pathway Clustering via Graph Mining Approach by Mahmud Shahriar Hossain and Team

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  1. Clustering PathwaysUsing Graph Mining Approach Mahmud Shahriar Hossain Monika Akbar Pramodh Pochu Venkata Sesha Sanagavarapu

  2. Design Pipeline Graph Objects of Pathways STKE Dataset Preprocessor Frequent Subgraph Discovery Mined Data Pathway Clustering NN Search Pathway Relations

  3. Dataset Properties (size)

  4. Dataset Properties (size)

  5. pf-ipf (tf-idf)

  6. Dataset Properties (pf-ipf)

  7. Dataset Properties (pf-ipf)

  8. Subgraph Discovery • What so novel about pruning edges? min_sup=2%

  9. Subgraph Discovery

  10. Subgraph Discovery

  11. Subgraph Discovery

  12. Subgraph Discovery Overall attempts saved = 89.52% Overall time saved = 99.39%

  13. Clustering

  14. Clustering

  15. Nearest Neighbors Cover Tree and Brute-force method

  16. Pathway Relations (StoryTelling) p1 p7 p2 p8 S T p3 p9 • Bidirectional Search

  17. Pathway Relations (StoryTelling)

  18. Pathway Relations (StoryTelling)

  19. Pathway Relations (StoryTelling)

  20. Questions ???

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