Novel EM based ML Kalman estimation framework for superresolution of stochastic three-states microtubule signal

Menon, Vineetha; Yarahmadian, Shantia; Rezania, Vahid

Novel EM based ML Kalman estimation framework for superresolution of stochastic three-states microtubule signal

Author

Menon, Vineetha

Yarahmadian, Shantia

Rezania, Vahid

Date

2018

Keywords

superresolution, Kalman filtering, expectation maximization, wavelets, principal component analysis, mutual information, missing data

Abstract (summary)

This work aims to address limited data availability and data/observation loss incurred due to non-uniform sampling of biological signals such as MTs. For this purpose, statistical modelling of stochastic MT signals using EM based ML driven Kalman estimation (MLK) is considered as a fundamental framework for prediction of missing MT observations. It was experimentally validated that the proposed superresolution methods provided superior overall performance, better MT signal estimation using fewer samples, high SNR, low errors, and better MT parameter estimation than other methods.

Publication Information

Vineetha Menon, Shantia Yarahmadian, Vahid Rezania. Novel EM based ML Kalman estimation framework for superresolution of stochastic three-states microtubule signal. BMC Syst Biol. 2018; 12(Suppl 6): 112. Published online 2018 Nov 22. doi:10.1186/s12918-018-0631-5