Decision-tree-based classification model and experimental data.

<p>Two peaks that identified using a decision-tree-based classification model are shown, with 2 cases misclassified into control groups. The data used here are the peaks selected through baseline subtraction, normalization, peak detection, and peak alignment of SELDI data obtained from 71 lung adenocarcinoma patients and 24 normal individuals.</p

Classification method based on principal components of SELDI spectral data and experimental data.

<p>Two cases and two normal individuals had been misclassified into opposite groups. The black squares indicate case individuals, and white squares with “V” shapes in the middle represent normal individuals. The data used here are the normalized SELDI data obtained from 71 lung adenocarcinoma patients and 24 normal individuals.</p

Candidate principal components.

a<p>Contribution of each PC to the whole variation.</p>b<p><i>P</i> value of the coefficient testing of logistic regression analysis on each PC.</p>c<p>Fitness index of each logistic regression model on single PC.</p

Features of lung adenocarcinoma patients.

a<p>Number of cases. The numbers in the parenthesis stand for the percentage.</p

M/Z means of cases and controls and the weights of PC1 and PC7 on the spectrum.

<p><b>A</b>) The M/Z means of cases (red) and normal controls (green) at each M/Z point. <b>B</b>) The weights of PC1 at each M/Z point. <b>C</b>) Weights of PC7 at each M/Z point. Horizontal lines in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034457#pone-0034457-g001" target="_blank">Figure 1B and 1C</a> represent 3*SD of corresponding PC on the spectrum. The data used here are the normalized SELDI data obtained from 71 lung adenocarcinoma patients and 24 normal individuals.</p

Cross-validation results of DT, SVM, LDA, CART, and our method.

<p>DT, decision-tree-based classification model; SVM, support vector machine; LDA, linear discriminant approach; CART, classification and regression tree.</p>a<p>The first line is the true positive rate (sensitivity); the second line is the true negative rate (specificity); and the third line is accuracy.</p

Summary of classification models based on principal components of SELDI spectral data.

<p>Summary of classification models based on principal components of SELDI spectral data.</p