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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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Generating Biological Network Motifs Sai Badey
Biological Networks • What are they? • Biological vs Regular Networks* • Terminology • Motifs • Vertices • Edges
Overall Scope • Focus on efficiency • Large networks • Network generation
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
Potential Results • No difference • Compare efficiencies of generation methods • Create a standard for network generation • Significant Difference • Determine which is more accurate
Steps • AmalaGhandi’s work • Looks through existing network • Determines motif • Expansion • Determine motif across several networks • Compare different networks & performance • Network Generation
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)
To Do • Function implementation • Combine with AmalaGhandi’s work • Learning • Vectors • Hash tables
Issues • Lots of research
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)
Questions? • Take node, change the order of the vertices