select_best_vs_worst

This function identifies the peptides from the function *appearance_to_binary*
that are 1 in one group and 0 or -1 in another group, and expands the grid
to all possible combinations.

This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) <doi:10.32614/RJ-2015-001>). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning.

Josep-Ramon Codina

peptoolkit

A Toolkit for Using Peptide Sequences in Machine Learning

select_best_vs_worst function

<dl><dt>appearance_best</dt>
<dd>A matrix with transformed counts for the 'best' group.</dd>
<dt>appearance_worst</dt>
<dd>A matrix with transformed counts for the 'worst' group.</dd></dl>

Arguments

Select Best vs Worst Peptides — select_best_vs_worst

<dl>

<dt>appearance_best</dt>
<dd>A matrix with transformed counts for the 'best' group.</dd>


<dt>appearance_worst</dt>
<dd>A matrix with transformed counts for the 'worst' group.</dd>

</dl>

select_best_vs_worst: Select Best vs Worst Peptides

Description

Usage

Value

Arguments

Examples