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Exploring Biological Network Motifs: Generating, Analyzing, and Comparing Networks

This project delves into the intricate world of biological network motifs by focusing on the generation, analysis, and comparison of networks. It aims to uncover the differences between biological and regular networks through methods like z-testing and motif searching. By implementing AmalaGhandi's work and utilizing concepts like vectors and hash tables, the study endeavors to establish a standard for network generation. The ultimate goal is to compare efficiencies of network generation methods and determine which approach yields more accurate results. With a keen focus on efficiency and large networks, this research project aims to shed light on the complex interplay of vertices and edges within biological networks.

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Exploring Biological Network Motifs: Generating, Analyzing, and Comparing Networks

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  1. Generating Biological Network Motifs Sai Badey

  2. Biological Networks • What are they? • Biological vs Regular Networks* • Terminology • Motifs • Vertices • Edges

  3. Overall Scope • Focus on efficiency • Large networks • Network generation

  4. Project Aim • Random Network Generation • functions (keep # vertices & # edges constant) • Swap the edges between outer-vertices • Completely random generation • Swap node degrees • Analysis & Comparisons • Run z-testing on the generations for small to extremely large graphs

  5. Potential Results • No difference • Compare efficiencies of generation methods • Create a standard for network generation • Significant Difference • Determine which is more accurate

  6. Steps • AmalaGhandi’s work • Looks through existing network • Determines motif • Expansion • Determine motif across several networks • Compare different networks & performance • Network Generation

  7. Current Status • Class Design • Network Class • Jung Library • Constructor, Copy constructor • Network generation • Analysis (z-test, motif searching, data collection) • Compare Networks Class • Equals • Comparison (highest degree node, motif comparison) • Analysis (significant difference, etc)

  8. To Do • Function implementation • Combine with AmalaGhandi’s work • Learning • Vectors • Hash tables

  9. Issues • Lots of research

  10. Sources • AmalaGhandi’s paper • SahandKhakabimamaghani, ImanSharafuddin, Norbert Dichter, Ina Koch,  Ali Masoudi-Nejad • QuateXelero: An Accelerated Exact Network Motif Detection Algorithm (Article) • Joseph Blitzstein and PersiDiaconis • A SEQUENTIAL IMPORTANCE SAMPLING ALGORITHM FOR GENERATING RANDOM GRAPHS WITH PRESCRIBED DEGREES (Article) • Bjorn H. Junker & Falk Schreiber • Analysis of Biological Networks (Book)

  11. Questions? • Take node, change the order of the vertices

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