A novel, fast, HMM-with-Duration implementation - for application with a new, pattern recognition informed, nanopore detector.
BACKGROUND
Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) is an HMM that can also exactly model the hidden-label length (recurrence) distributions - while the regular HMM will impose a best-fit geometric distribution in its modeling/representation.


RESULTS
A Novel, Fast, HMM-with-Duration (HMMwD) Implementation is presented, and experimental results are shown that demonstrate its performance on two-state synthetic data designed to model Nanopore Detector Data. The HMMwD experimental results are compared to (i) the ideal model and to (ii) the conventional HMM. Its accuracy is clearly an improvement over the standard HMM, and matches that of the ideal solution in many cases where the standard HMM does not. Computationally, the new HMMwD has all the speed advantages of the conventional (simpler) HMM implementation. In preliminary work shown here, HMM feature extraction is then used to establish the first pattern recognition-informed (PRI) sampling control of a Nanopore Detector Device (on a "live" data-stream).


CONCLUSIONS
The improved accuracy of the new HMMwD implementation, at the same order of computational cost as the standard HMM, is an important augmentation for applications in gene structure identification and channel current analysis, especially PRI sampling control, for example, where speed is essential. The PRI experiment was designed to inherit the high accuracy of the well characterized and distinctive blockades of the DNA hairpin molecules used as controls (or blockade "test-probes"). For this test set, the accuracy inherited is 99.9%.
Winters-Hilt
Stephen
S
Dept. of Computer Science, University of New Orleans, New Orleans, LA 70148, USA. winters@cs.uno.edu
Baribault
Carl
C
D000465:Algorithms; D052157:Aptamers, Nucleotide / Q000032:analysis* / Q000276:immunology; D001185:Artificial Intelligence; D001427:Bacterial Toxins / Q000737:chemistry*; D015374:Biosensing Techniques / Q000295:instrumentation*; D003627:Data Interpretation, Statistical; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006460:Hemolysin Proteins / Q000737:chemistry*; D007700:Kinetics; D008390:Markov Chains; D036103:Nanotechnology / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D016062:Porosity; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers
D052157:Aptamers, Nucleotide; D001427:Bacterial Toxins; D006460:Hemolysin Proteins; C021091:staphylococcal alpha-toxin