Reconstructing networks using co-temporal functions

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. Categories and Subject Descriptors

• Type: Conference
• Year: 2006
• Where: ACMSE
• Authors: Edward E. Allen, Anthony Pecorella, Jacquelyn S. Fetrow, David J. John, William Turkett
• Keywords: ACMSE 2006, Co-temporal Functions, Difficult Inverse Problem, Theoretical Computer Science, Time Series Data