Classification of solutions of higher order conformally invariant equations

1999 ◽  
Vol 313 (2) ◽  
pp. 207-228 ◽  
Author(s):  
Juncheng Wei ◽  
Xingwang Xu
Keyword(s):  
Higher Order ◽  
Conformally Invariant ◽  
Classification Of Solutions

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 Classification of solutions for guided waves in anisotropic composites with large numbers of layers

2018 ◽  
Vol 144 (6) ◽  
pp. 3236-3251 ◽  
Author(s):  
Armin M. A. Huber ◽  
Markus G. R. Sause
Keyword(s):  
Guided Waves ◽  
Large Numbers ◽  
Classification Of Solutions

scholarly journals Chemometric outlier classification of 2D-NMR spectra to enable higher order structure characterization of protein therapeutics

2020 ◽  
Vol 199 ◽  
pp. 103973 ◽  
Author(s):  
David A. Sheen ◽  
Vincent K. Shen ◽  
Robert G. Brinson ◽  
Luke W. Arbogast ◽  
John P. Marino ◽  
...  
Keyword(s):  
Nmr Spectra ◽  
2D Nmr ◽  
Higher Order ◽  
Protein Therapeutics ◽  
Structure Characterization ◽  
Order Structure ◽  
2D Nmr Spectra
Sign in / Sign up

Export Citation Format

Share Document