Code Pattern Analysis is a Python code that runs on a Jupyter Notebook. the script can identify coarse-grained amino acid patterns in a peptide library according to the Kyte-Doolittle classification. A model dataset "input.xlsx" that was applied in the corresponding publication is provided as input data. Code PAAA (Peptide Amino Acid Analyzer) can quantify the numbers of certain amino acids in a peptide library. A model dataset "Peptide-Library163.csv" and "Rel-Pep-163.csv" that was applied in the corresponding publication are provided as input data
Abstract: In nature, protein chain interactions (Pro-ChInt) of single- / multi-protein, a common but...
International audienceAbstract Motivation The objective is to diagnose dynamics perturbations caused...
Explainable and interpretable unsupervised machine learning helps one to understand the underlying s...
The application of data mining techniques into biological data is well established. The aim of this ...
We describe several protein sequence statistics designed to evaluate distinctive attributes of resid...
Data Mining has recently increased its popularity of solving crucial problems in the field of biolog...
We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs ...
Recent advances in high-throughput technologies have made it possible to generate both gene and prot...
The primary structures containing data and analysis products are peptidereads_fig2.mat (for figure 2...
These files contain data, sequences and supplementary information for the article entitled 'Characte...
Summary: Sequence-derived structural and physiochemical features have been frequently used for analy...
Summary: Sequence-derived structural and physiochemical features have been frequently used for analy...
Recent advances in high-throughput technologies have made it possible to generate both gene and prot...
A data set composed of 1141 proteins representative of all eukaryotic protein sequences in the Swiss...
Code S1 is a Python script for calculating the amino acids composition of charged, hydrogen bonding,...
Abstract: In nature, protein chain interactions (Pro-ChInt) of single- / multi-protein, a common but...
International audienceAbstract Motivation The objective is to diagnose dynamics perturbations caused...
Explainable and interpretable unsupervised machine learning helps one to understand the underlying s...
The application of data mining techniques into biological data is well established. The aim of this ...
We describe several protein sequence statistics designed to evaluate distinctive attributes of resid...
Data Mining has recently increased its popularity of solving crucial problems in the field of biolog...
We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs ...
Recent advances in high-throughput technologies have made it possible to generate both gene and prot...
The primary structures containing data and analysis products are peptidereads_fig2.mat (for figure 2...
These files contain data, sequences and supplementary information for the article entitled 'Characte...
Summary: Sequence-derived structural and physiochemical features have been frequently used for analy...
Summary: Sequence-derived structural and physiochemical features have been frequently used for analy...
Recent advances in high-throughput technologies have made it possible to generate both gene and prot...
A data set composed of 1141 proteins representative of all eukaryotic protein sequences in the Swiss...
Code S1 is a Python script for calculating the amino acids composition of charged, hydrogen bonding,...
Abstract: In nature, protein chain interactions (Pro-ChInt) of single- / multi-protein, a common but...
International audienceAbstract Motivation The objective is to diagnose dynamics perturbations caused...
Explainable and interpretable unsupervised machine learning helps one to understand the underlying s...