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S. Cerevisiae Revisiting Parameter Estimation in Biological Networks: Influence of Symmetries Jithin K. Sreedharan Purdue University Krzysztof Wojceich Turowski Szpankowski (Purdue) (Purdue) BioKDD August 5, 2019 <latexit

  1. S. Cerevisiae Revisiting Parameter Estimation in Biological Networks: Influence of Symmetries Jithin K. Sreedharan Purdue University Krzysztof Wojceich Turowski Szpankowski (Purdue) (Purdue) BioKDD August 5, 2019

  2. <latexit 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The Problem: Fitting a model Data stream or Parameter fitting Dynamic graph fixed data of interactions 1 X <--> Y 2 Estimated Model X <--> A 4 3 parameters C <--> M 9 5 6 Pr( G n | G n 0 ; θ ) 𝐻 8 11 7 Parameters of the model 10 12 Observed Seed graph graph Data usually represents a single snapshot of the graph of dynamic § G obs := G n evolution G n , G n − 1 , . . . , G n 0 Random graph models tailored to specific applications provide deep insights § unlike general learning models Examples: asymptotic behavior, clustering properties, properties of motifs § (subgraphs or lower/higher order structures), diffusion over the graph etc Jithin K. Sreedharan BioKDD'19 2

  3. Why need to revisit the estimation methods? Symmetries of the graph § Most of the existing parameter estimation techniques overlook the critical property of graph symmetry (also known formally as graph automorphisms). § The estimated parameters give statistically insignificant results concerning the observed network Goal-1: Take into account the number of automorphisms of the observed network to restrict the parameter search to a more meaningful range Parameter estimation methods § Existing methods heavily depend upon stead-state assumption and asymptotic properties of the graph model § Many of these assumptions has been proven not to exist or exist with strong conditions Goal-2: Use exact non-asymptotic relations Jithin K. Sreedharan BioKDD'19 3

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A comparative study of Gaussian Graphical Model approaches for genomic data Roberto Anglani Institute of Intelligent Systems for Automation, CNR-ISSIA, Bari, Italy in collaboration with PF Stifanelli, TM Creanza, VC Liuzzi, S Mukherjee, N Ancona

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CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of Electrical

CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of Electrical

. LCE . . . . . . . Preamble Repeats Generalized Suffjx Tree More Repeats LCA Preamble . Repeats Generalized Suffjx Tree More Repeats LCA LCE CSI5126 . Algorithms in bioinformatics Suffjx Trees Marcel Turcotte School of

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