SVD_PSSM

pssm_name

<p>Singular value decomposition is a general purpose matrix factorization approach
that has many useful applications in signal processing and statistics. In this function SVD is
applied to a matrix representation of a protein with the aim of reducing its dimensionality
Given an input matrix Mat with dimensions N*M SVD is used to calculate its factorization
of the form: \(Mat=U\Sigma V,\) where \(\Sigma\) is a diagonal matrix whose diagonal
entries are known as the singular values of Mat. The resulting descriptor is the ordered
set of singular values: \(SVD\in\mathcal{R}^L,\) where L=min(M,N).
and here <code><a rd-options="base" href="/link/svd?package=PSSMCOOL&version=0.2.4&to=base" data-mini-rdoc="base::svd">svd</a></code> function is used for this purpose.</p>

Returns almost all features that has been extracted from Position Specific
Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length)
and 20 columns produced by 'PSI-BLAST' which is a program to produce
PSSM Matrix from multiple sequence alignment of proteins
see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some
of these features are described in Zahiri, J., et al.(2013)
<DOI:10.1016/j.ygeno.2013.05.006>,
Saini, H., et al.(2016)
<DOI:10.17706/jsw.11.8.756-767>,
Ding, S., et al.(2014)
<DOI:10.1016/j.biochi.2013.09.013>,
Cheng, C.W., et al.(2008)
<DOI:10.1186/1471-2105-9-S12-S6>,
Juan, E.Y., et al.(2009)
<DOI:10.1109/CISIS.2009.194>.

Alireza mohammadi

PSSMCOOL

Features Extracted from Position Specific Scoring Matrix (PSSM)

SVD_PSSM function

<p>Singular value decomposition is a general purpose matrix factorization approach
that has many useful applications in signal processing and statistics. In this function SVD is
applied to a matrix representation of a protein with the aim of reducing its dimensionality
Given an input matrix Mat with dimensions N*M SVD is used to calculate its factorization
of the form: \(Mat=U\Sigma V,\) where \(\Sigma\) is a diagonal matrix whose diagonal
entries are known as the singular values of Mat. The resulting descriptor is the ordered
set of singular values: \(SVD\in\mathcal{R}^L,\) where L=min(M,N).
and here <code><a rd-options='base' href='svd'>svd</a></code> function is used for this purpose.</p>

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SVD_PSSM: Singular Value Decomposition (SVD)

Description

Usage

Arguments

Value

References

Examples