Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Abrupt reduction/resumption of thermal fluctuations of a force probe has been used to identify association/dissociation events of protein–ligand bonds.  show that off-rate of molecular dissociation can be estimated by the analysis of the bond lifetime, while the on-rate of molecular association can be estimated by the analysis of the waiting time beten two neighboring bond events. Hover, the analysis relies heavily on subjective judgments and is time-consuming. To automate the process of mapping out bond events from thermal fluctuation data,  develop a hidden Markov model (HMM)-based method.#R##N##R##N#Results: The HMM method represents the bond state by a hidden variable with two values: bound and unbound. The bond association/dissociation is visualized and pinpointed.  apply the method to analyze a key receptor–ligand interaction in the early stage of hemostasis and thrombosis: the von Willebrand factor (VWF) binding to platelet glycoprotein Ibα (GPIbα). The numbers of bond lifetime and waiting time events estimated by the HMM are much more than those estimated by a descriptive statistical method from the same set of raw data. The kinetic parameters estimated by the HMM are in excellent agreement with those by a descriptive statistical analysis, but have much smaller errors for both wild-type and two mutant VWF-A1 domains. Thus, the computerized analysis allows us to speed up the analysis and improve the quality of estimates of receptor–ligand binding kinetics.#R##N##R##N#Contact:jeffwu@isye.gatech.edu or cheng.zhu@bme.gatech.edu
['Lining Ju', 'Yijie Dylan Wang', 'Ying Hung', 'Chien-Fu Jeff Wu', 'Cheng Zhu']
I am writing a research paper on a idea An HMM-based algorithm for evaluating rates of receptor–ligand binding kinetics from thermal fluctuation data
[{"from": "human", "value": "I am writing a research paper on a idea An HMM-based algorithm for evaluating rates of receptor\u2013ligand binding kinetics from thermal fluctuation data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Abrupt reduction/resumption of thermal fluctuations of a force probe has been used to identify association/dissociation events of protein\u2013ligand bonds.  show that off-rate of molecular dissociation can be estimated by the analysis of the bond lifetime, while the on-rate of molecular association can be estimated by the analysis of the waiting time beten two neighboring bond events. Hover, the analysis relies heavily on subjective judgments and is time-consuming. To automate the process of mapping out bond events from thermal fluctuation data,  develop a hidden Markov model (HMM)-based method.#R##N##R##N#Results: The HMM method represents the bond state by a hidden variable with two values: bound and unbound. The bond association/dissociation is visualized and pinpointed.  apply the method to analyze a key receptor\u2013ligand interaction in the early stage of hemostasis and thrombosis: the von Willebrand factor (VWF) binding to platelet glycoprotein Ib\u03b1 (GPIb\u03b1). The numbers of bond lifetime and waiting time events estimated by the HMM are much more than those estimated by a descriptive statistical method from the same set of raw data. The kinetic parameters estimated by the HMM are in excellent agreement with those by a descriptive statistical analysis, but have much smaller errors for both wild-type and two mutant VWF-A1 domains. Thus, the computerized analysis allows us to speed up the analysis and improve the quality of estimates of receptor\u2013ligand binding kinetics.#R##N##R##N#Contact:jeffwu@isye.gatech.edu or cheng.zhu@bme.gatech.edu"}]