INFERENCE OF GENE REGULATORY NETWORKS BY MAXIMUM-LIKELIHOOD ADAPTIVE FILTERING AND DISCRETE FISH SCHOOL SEARCH Conference Paper uri icon

abstract

  • 2018 IEEE. We propose a new algorithm for inference of gene regulatory networks (GRN) from noisy gene expression data based on maximum-likelihood (ML) adaptive filtering and the discrete fish school search algorithm (DFSS). The approach is based on the general partially-observed Boolean dynamical system (POBDS) model, and as such can be used for simultaneous state and parameter estimation for any Boolean dynamical system observed in noise. The proposed DFSS-ML-BKF algorithm combines the ML adaptive Boolean Kalman Filter (ML-BKF) with DFSS, a version of the Fish School Search algorithm tailored for discrete parameter spaces. Results based on synthetic gene expression time-series data using the well-known p53-MDM2 negative-feedback loop GRN demonstrate that DFSS-ML-BKF can infer the network topology accurately and efficiently.

name of conference

  • 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)

published proceedings

  • 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)

author list (cited authors)

  • Tan, Y., Neto, F., & Neto, U. B.

citation count

  • 3

complete list of authors

  • Tan, Yukun||Neto, Fernando B Lima||Neto, Ulisses Braga

publication date

  • January 2018