preprocessing

Given the results the data is rescaled for values between
0 and 1, so that the length of the sequences does not influence the results.
The rescaling of the sequences are made separately

It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples.

Fabricio Martins Lopes

BASiNETEntropy

Classification of RNA Sequences using Complex Network and
Information Theory

Murilo Montanini Breve

Matheus Henrique Pimenta-Zanon

preprocessing function

<dl><dt>datah</dt>
<dd>Array with results numerics</dd>
<dt>tamM</dt>
<dd>Integer number of mRNA sequences</dd>
<dt>tamLNC</dt>
<dd>Integer number of lncRNA sequences</dd>
<dt>tamSNC</dt>
<dd>Integer number of sncRNA sequences</dd></dl>

Arguments

Author

Rescales the results between values from 0 to 1 — preprocessing

<dl>

<dt>datah</dt>
<dd>Array with results numerics</dd>


<dt>tamM</dt>
<dd>Integer number of mRNA sequences</dd>


<dt>tamLNC</dt>
<dd>Integer number of lncRNA sequences</dd>


<dt>tamSNC</dt>
<dd>Integer number of sncRNA sequences</dd>

</dl>

preprocessing: Rescales the results between values from 0 to 1

Description

Usage

Value

Arguments

Author