Information theoretic-based Biological Network Analysis

Document Type

Presentation Abstract

Presentation Date

3-9-2020

Abstract

Biological networks are complex and often contain nonlinear interactions among a usually large number of species, genes, nutrients, metabolites, .... Correlation coefficients are widely used to analyze “omics” data as measures of linear interactions. However, how would we detect dependence when data is non-linear? In this talk, I will use mutual information based graph theory to analyze microbiome network and introduce a method to find a partition between contaminants and true bacteria that minimizes the loss of information. Among all the possible partitions of a network, this can be considered an optimal partition for characterizing the underlying structures of the network. Time permitting, I will briefly, discuss other projects that I have been working on.

Additional Details

March 9, 2020 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

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