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Hidden Markov Analysis of Trajectories in Single‐Molecule Experiments and the Effects of Missed Events
Author(s) -
Stigler Johannes,
Rief Matthias
Publication year - 2012
Publication title -
chemphyschem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.016
H-Index - 140
eISSN - 1439-7641
pISSN - 1439-4235
DOI - 10.1002/cphc.201100814
Subject(s) - hidden markov model , markov chain , markov model , computer science , statistical physics , scalability , molecule , markov process , statistical analysis , data mining , algorithm , theoretical computer science , biological system , chemistry , machine learning , artificial intelligence , mathematics , statistics , physics , organic chemistry , biology , database
The ever more complex fluctuation patterns discovered by single molecule experiments require statistical methods to analyze multi‐state hopping traces of long lengths. Hidden Markov modeling is a statistical tool that offers the scalability to analyze even complex data and extract kinetic information. We give an introduction on how to implement hidden Markov modeling for the analysis of single molecule force spectroscopic traces, deal with missed events, and test the method on a calcium binding protein.