TY - GEN
T1 - Learning and Estimation of Single Molecule Behavior
AU - Rajaganapathy, Sivaraman
AU - Melbourne, James
AU - Aggarwal, Tanuj
AU - Shrivastava, Rachit
AU - Salapaka, Murti V.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - Data analysis in single molecule studies often involves estimation of parameters and the detection of abrupt changes in measured signals. For single molecule studies, tools for automated analysis that are crucial for rapid progress, need to be effective under large noise magnitudes, and often must assume little or no prior knowledge of parameters being studied. This article examines an iterated, dynamic programming based step detection algorithm (SDA). It is established that given a prior estimate, an iteration of the SDA necessarily improves the estimate. The analysis provides an explanation and a confirmation of the effectiveness of the learning and estimation capabilities of the algorithm observed empirically. Further, an alternative application of the SDA is demonstrated, wherein the parameters of a worm-like chain (WLC) model are estimated, for the automated analysis of data from single molecule protein pulling experiments.
AB - Data analysis in single molecule studies often involves estimation of parameters and the detection of abrupt changes in measured signals. For single molecule studies, tools for automated analysis that are crucial for rapid progress, need to be effective under large noise magnitudes, and often must assume little or no prior knowledge of parameters being studied. This article examines an iterated, dynamic programming based step detection algorithm (SDA). It is established that given a prior estimate, an iteration of the SDA necessarily improves the estimate. The analysis provides an explanation and a confirmation of the effectiveness of the learning and estimation capabilities of the algorithm observed empirically. Further, an alternative application of the SDA is demonstrated, wherein the parameters of a worm-like chain (WLC) model are estimated, for the automated analysis of data from single molecule protein pulling experiments.
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U2 - 10.23919/ACC.2018.8431534
DO - 10.23919/ACC.2018.8431534
M3 - Conference contribution
AN - SCOPUS:85052568232
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 5125
EP - 5130
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -