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Title: INFERENCE OF GENE REGULATORY NETWORKS BY MAXIMUM-LIKELIHOOD ADAPTIVE FILTERING AND DISCRETE FISH SCHOOL SEARCH
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.  more » « less
Award ID(s):
1718924
PAR ID:
10109171
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
Page Range / eLocation ID:
1 to 6
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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