hmm_LPC

This feature uses linear predictive coding (LPC) to map each HMM to a \(20 \times 14 = 280\) dimensional vector,
where for each of the 20 columns of the HMM, LPC is used to extract a 14 dimensional vector \(D_n\)

Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins.
Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take
advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012)
<DOI:10.1038/nmeth.1818> and 'HMMer' Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by
this package can be used for protein fold classification, protein structural class prediction,
sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features
extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>,
Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.

Shayaan Emran

protHMM

Protein Feature Extraction from Profile Hidden Markov Models

hmm_LPC function

<dl><dt>hmm</dt>
<dd>The name of a profile hidden markov model file.</dd></dl>

Arguments

hmm_LPC — hmm_LPC

<dl>

<dt>hmm</dt>
<dd>The name of a profile hidden markov model file.</dd>

</dl>

hmm_LPC: hmm_LPC

Description

Usage

Value

Arguments

References

Examples