hmm_svd

This feature uses singular value decomposition (SVD) to reduce the dimensionality of the inputted hidden
markov model matrix. SVD factorizes a matrix C of dimensions \(i, j\) to \(U[i, r] \times \Sigma[r, r] \times V[r, j]\).
The diagonal values of \(\Sigma\) are known as the singular values of matrix C, and are what are returned with this function.

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_svd function

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

Arguments

hmm_svd — hmm_svd

<dl>

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

</dl>

hmm_svd: hmm_svd

Description

Usage

Value

Arguments

References

Examples