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Extracting synergistic gene subnetworks from pairwise gene data

This study presents a method for extracting synergistic gene subnetworks from pairwise gene data in C. elegans. With over 388,000 gene pairs, identifying similar P-values across triplets, quadruplets, and more is computationally challenging. The alternative approach involves creating a degree-constrained subgraph from an interconnected graph of genes based on P-values. Feature selection techniques like Pearson correlation reduced the original set of 441 genes to 274, pinpointing a group of 54 key genes. The degree-constrained subgraph optimization problem is tackled through an iterative procedure that prioritizes highly interconnected subgraphs with edge density greater than twice the original. Avoiding independencies using Bayes' Ball identified specific gene groups like Post-F59B2.13 and Pre-ric-19. Convergence was achieved in computing the highest interconnected sub-graphs and results revealed insightful gene interactions.

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Extracting synergistic gene subnetworks from pairwise gene data

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  1. Extracting synergistic gene subnetworks from pairwise gene data ALEXANDROS ILIADIS

  2. Problem Discription –Input Data • 882 x 882 Weigth Matrix • Element i,k is the P(Value) that Gi ,Gk interact towards Synapse creation in C. elegans

  3. Problem Description All gene pairs 388521 IF we wanted similar Pvalues for : • Triplets 113966160 • Quadruplets 2.5044e+010 • More….Computationally Intractable • SO….. • Alternative approach in identifying Important Synergistic Subnetworks

  4. Approach • Consider the fully Interconnected graph • Nodes:Genes • Edges:P-values • Create Degree Constrained Subgraph • Extract Significantly interconnected subneworks • Bayes Ball to avoid independencies

  5. Feature Selection • Pearson Correlation • Found 167 correlate features so the original set of 441 was reduced to 274 • Also identified a group of 54 genes

  6. Degree Constrained Subgraph

  7. Optimization Problem

  8. Iterative Procedure Followed

  9. Example

  10. Convergence

  11. Convergence (cont’d)

  12. Highest Interconnected Sub-graphs • Exhaustive search for highly Interconnected Subgraphs • Graphs with the largest edge density • Density>2*Original density

  13. Avoid Independencies-Bayes’ Ball 3 2 4 1 Nodes 1 and 4 are independent given 2 and 3

  14. Results Group Post F59B2.13 Post- Flp-21 Group Pre ric-19 Group- Pre C02C2.4

  15. Results Post Ace-3 Post- Fbxb-103 Pre mai-3 Post Lim-4

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