Parsimonious higher order Markov models for rating transitions

2017 ◽  
Vol 181 (1) ◽  
pp. 107-131 ◽  
Author(s):  
S. Baena‐Mirabete ◽  
P. Puig
Keyword(s):  
Markov Models ◽  
Higher Order ◽  
Rating Transitions

Higher-order Markov models for metagenomic sequence classification

2020 ◽  
Vol 36 (14) ◽  
pp. 4130-4136
Author(s):  
David J Burks ◽  
Rajeev K Azad
Keyword(s):  
Dna Sequences ◽  
Markov Models ◽  
Fragment Size ◽  
Higher Order ◽  
Training Data ◽  
Supplementary Information ◽  
Local Alignment ◽  
Metagenomic Sequence ◽  
Higher Order Models

Abstract Motivation Alignment-free, stochastic models derived from k-mer distributions representing reference genome sequences have a rich history in the classification of DNA sequences. In particular, the variants of Markov models have previously been used extensively. Higher-order Markov models have been used with caution, perhaps sparingly, primarily because of the lack of enough training data and computational power. Advances in sequencing technology and computation have enabled exploitation of the predictive power of higher-order models. We, therefore, revisited higher-order Markov models and assessed their performance in classifying metagenomic sequences. Results Comparative assessment of higher-order models (HOMs, 9th order or higher) with interpolated Markov model, interpolated context model and lower-order models (8th order or lower) was performed on metagenomic datasets constructed using sequenced prokaryotic genomes. Our results show that HOMs outperform other models in classifying metagenomic fragments as short as 100 nt at all taxonomic ranks, and at lower ranks when the fragment size was increased to 250 nt. HOMs were also found to be significantly more accurate than local alignment which is widely relied upon for taxonomic classification of metagenomic sequences. A novel software implementation written in C++ performs classification faster than the existing Markovian metagenomic classifiers and can therefore be used as a standalone classifier or in conjunction with existing taxonomic classifiers for more robust classification of metagenomic sequences. Availability and implementation The software has been made available at https://github.com/djburks/SMM. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Extracting Information from Spot Interest Rates and Credit Ratings using Double Higher-Order Hidden Markov Models

2007 ◽  
Vol 29 (3-4) ◽  
pp. 425-425
Author(s):  
Tak-Kuen Siu ◽  
Wai-Ki Ching ◽  
Eric S. Fung ◽  
Michael K. Ng
Keyword(s):  
Interest Rates ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Credit Ratings ◽  
Higher Order

scholarly journals Predicting the Affordable Rate in Interference-Limited Cellular Systems Using Higher-Order Markov Models

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 4730-4748 ◽  
Author(s):  
Saishankar Katri Pulliyakode ◽  
Sheetal Kalyani ◽  
Lajos Hanzo ◽  
K. Giridhar
Keyword(s):  
Markov Models ◽  
Higher Order ◽  
Cellular Systems
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