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Defining Dysregulated Subnetworks in Cancer: A Mutual Information Approach

This article presents a method for defining dysregulated subnetworks in cancer using mutual information and entropy measures. It discusses the theoretical framework, including concepts like high and low entropy, along with a systematic approach to evaluate state functions without redundancy. The CRANE algorithm is introduced for ranking subnetworks, enabling efficient neural network classification for predicting metastasis. The work addresses the trade-off between complexity and runtime, highlighting the advantages and shortcomings of combinatorial dysregulation.

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Defining Dysregulated Subnetworks in Cancer: A Mutual Information Approach

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  1. Subnetwork State Functions Define Dysregulated Subnetworks in Cancer Salim A. Chowdhury, Rod K. Nibbe, Mark R.Chance, Mehmet Koyuturk JCB 2011

  2. Previous Work

  3. Mutual Information • Entropy H(X) • Mutual Information I(Y;X) = H(X)-H(X|Y) high entropy low entropy low mutual information high mutual information

  4. 0 0 1 1 0 0 1 1 0 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 Mutual Information – Dysregulation Measure C

  5. Combinatorial Coordinate Dysregulation State function is considered informative if: • There is no redundancy in the state function • for every

  6. Pruning the search space Where • Notice that Jbound() is not J() • This result will help us decide if we would like to extend subnetwork S. If we will not extend S.

  7. 0 0 1 1 1 0 KRANE Algorithm • S is extensible if • From the possible extension we choose to further check only b extensions with the top J() value. • Stop extending S if |S|>d.

  8. Complexity • Complexity is exponential in d. • To make sure we don’t miss subnetworks we should use • Using Jbound()we could prune the search space thus reducing running time without loosing results.

  9. CRANE Runtime

  10. Neural Network Classification • Rank the subnetwork according to I(FS;C)and take the top K ranked subnetworks that are not overlapping. • Use these Network as input for NN that predicts metastasis.

  11. Effect of parameters

  12. Conclusion Advantages: • Combinatorial dysregulation. • More sophisticated heuristics base on theoretical bound (“almost” exhaustive search). Shortcomings: • Runtime is exponential in d so we could check only relatively small networks. • Even for small values of d we have dimensionality problems. • No post-processing that tries to merge subnetworks. • Dismissing of overlapping subnetworks.

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