<p>This table presents the number of algorithms that selected the attribute. Weighting algorithms were PCA, SVM, Relief, Uncertainty, Gini index, Chi Squared, Deviation, Rule, Correlation, and Information Gain.</p
<p>Pie chart representing the distribution of the identified proteins based on their molecular funct...
<p>Simulations of total variability resulting from different random correlation matrices selected f...
The potential for obtaining a true mass spectrometric protein identification result depends on the c...
<p>The attribute weighting models and the numbers of important protein features selected by each mod...
<p>The numbers and the averages of most important alleles (fragments) selected by different attribut...
Abstract Finding reliable, meaningful patterns in data with high numbers of at-tributes can be extre...
Protein classification by machine learning algorithms is now widely used in structural and functiona...
As a kind of preprocessing technique commonly used on high-dimensional data, feature selection reduc...
The potential for obtaining a true mass spectrometric protein identification result depends on the c...
<p>The table compiles the name under which a method is most often referred to, the type of evolution...
<p>Phobius and Philius protein type classification performance on the development set: for each prot...
The present research examines a wide range of attribute selection methods – 86 methods that include ...
<p>(A) Biplots of PCA of seven observations which predicted to the space defined by the first and se...
<p>Multivariate data (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104970#...
<p>Cases (A) and (C) for general dataset of proteins and cases (B) and (D) for re-refined protein st...
<p>Pie chart representing the distribution of the identified proteins based on their molecular funct...
<p>Simulations of total variability resulting from different random correlation matrices selected f...
The potential for obtaining a true mass spectrometric protein identification result depends on the c...
<p>The attribute weighting models and the numbers of important protein features selected by each mod...
<p>The numbers and the averages of most important alleles (fragments) selected by different attribut...
Abstract Finding reliable, meaningful patterns in data with high numbers of at-tributes can be extre...
Protein classification by machine learning algorithms is now widely used in structural and functiona...
As a kind of preprocessing technique commonly used on high-dimensional data, feature selection reduc...
The potential for obtaining a true mass spectrometric protein identification result depends on the c...
<p>The table compiles the name under which a method is most often referred to, the type of evolution...
<p>Phobius and Philius protein type classification performance on the development set: for each prot...
The present research examines a wide range of attribute selection methods – 86 methods that include ...
<p>(A) Biplots of PCA of seven observations which predicted to the space defined by the first and se...
<p>Multivariate data (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104970#...
<p>Cases (A) and (C) for general dataset of proteins and cases (B) and (D) for re-refined protein st...
<p>Pie chart representing the distribution of the identified proteins based on their molecular funct...
<p>Simulations of total variability resulting from different random correlation matrices selected f...
The potential for obtaining a true mass spectrometric protein identification result depends on the c...
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