Add to ORKGResearch on cleavage site prediction for signal peptides has focused mainly on the application of different classification algorithms to achieve improved prediction accuracies. This paper addresses the fundamental issue of amino acid encoding to present amino acid sequences in the most beneficial way for machine learning algorithms. A comparison of several standard encoding methods shows, that for cleavage site prediction the frequently used orthonormal encoding is inferior compared to other methods. The best results are achieved with a new encoding method named BLOMAP – based on the BLOSUM62 substitution matrix – using a Naïve Bayes classifier
Trypsin is the most used enzyme in proteomics experiments to convert proteins into peptides as it ha...
Signal peptides are short, cleavable, N-terminal peptides (from 15 up to 50 residue long) that are p...
Background\ud Ubiquitination is a very important process in protein post-translational modification,...
Research on cleavage site prediction for signal peptides has focused mainly on the application of di...
Research on cleavage site prediction for signal peptides has focused mainly on the application of di...
Neural networks are often used in protein sequence analysis. However, the results are unreliable, ma...
A challenging problem in data mining is the application of efficient techniques to automatically ann...
signal peptide prediction accuracy by simulated neural network I.Ladunga1, F.Czakd2, I.Csabai2 and T...
Research on peptide classification problems has focused mainly on the study of different encodings a...
Motivation: Automatic recognition of signal peptides and cleavage sites in proteins is a topical iss...
Background: Amino-terminal signal peptides (SPs) are short regions that guide the targeting of secre...
Research on peptide classification problems has focused mainly on the study of different encodings a...
We have developed a new method for identification of signal peptides and their cleavage sites based ...
We have developed a new method for the identification of signal peptides and their cleavage sites ba...
We present here a neural network-based method for detection of signal peptides (abbreviation used: S...
Trypsin is the most used enzyme in proteomics experiments to convert proteins into peptides as it ha...
Signal peptides are short, cleavable, N-terminal peptides (from 15 up to 50 residue long) that are p...
Background\ud Ubiquitination is a very important process in protein post-translational modification,...
Research on cleavage site prediction for signal peptides has focused mainly on the application of di...
Research on cleavage site prediction for signal peptides has focused mainly on the application of di...
Neural networks are often used in protein sequence analysis. However, the results are unreliable, ma...
A challenging problem in data mining is the application of efficient techniques to automatically ann...
signal peptide prediction accuracy by simulated neural network I.Ladunga1, F.Czakd2, I.Csabai2 and T...
Research on peptide classification problems has focused mainly on the study of different encodings a...
Motivation: Automatic recognition of signal peptides and cleavage sites in proteins is a topical iss...
Background: Amino-terminal signal peptides (SPs) are short regions that guide the targeting of secre...
Research on peptide classification problems has focused mainly on the study of different encodings a...
We have developed a new method for identification of signal peptides and their cleavage sites based ...
We have developed a new method for the identification of signal peptides and their cleavage sites ba...
We present here a neural network-based method for detection of signal peptides (abbreviation used: S...
Trypsin is the most used enzyme in proteomics experiments to convert proteins into peptides as it ha...
Signal peptides are short, cleavable, N-terminal peptides (from 15 up to 50 residue long) that are p...
Background\ud Ubiquitination is a very important process in protein post-translational modification,...