Reconstructing networks using co-temporal functions

ABSTRACT

Reconstructing networks from time series data is a difficult inverse problem. We apply two methods to this problem using co-temporal functions. Co-temporal functions capture mathematical invariants over time series data. Two modeling techniques for co-temporal networks, one based on algebraic techniques and the other on Bayesian inference, are compared and contrasted on simulated biological network data.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.1 SYMBOLIC AND ALGEBRAIC MANIPULATION
      I.1.2 Algorithms

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES

Keywords:
Bayesian modeling, algebraic modeling, computational biology