36 research outputs found

    Supplementary documents S1-S5.

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    This file contains supporting documents from S1 to S5. Supplementary Document S1: SVM training for two class classification; Supplementary Document S2: Sliding Windows Size technique; Supplementary Document S3: List of VOC and their common names; Supplementary Document S4: Distribution of Key VOCs; Supplementary Document S5: Principal Component plots for visualizing patient classes. (DOCX)</p

    Outline of the Boot-SVM-RFE technique for significant VOC selection.

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    Data matrix has N rows as VOCs with concentrations of nmole/L and M columns as patients/samples. B is the number of bootstrap samples drawn from the data matrix. Vb and Rb’s are the VOC ranked list and VOC rank-score for the bth bootstrap sample. Null hypothesis represents ith VOC is not important for patients’ classification. α is the desired level of significance.</p

    Effects of the VOCs on lung cancer classifications.

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    Effects of the VOCs on lung cancer classifications.</p

    Ranking of significant VOCs on different patients’ classification.

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    Ranking of significant VOCs on different patients’ classification.</p

    Classification metrics for different combinations of VOCs.

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    Classification metrics for different combinations of VOCs.</p

    Outline of the experimental approach used in this study.

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    Various steps undertaken in this study are shown in flow chart form. The various steps include (i) capturing of exhaled breath; (ii) capturing VOCs present in exhaled breath samples; (iii) generation of molecular concentration data (nmole/L) of VOCs through bioassay mass-spectrometry technique; (iv) feature (VOC) selection and classification model training and its application for lung cancer detection; and (v) validation of the selected VOCs through classification accuracy, literature search, and expert opinion.</p

    Summary statistic(s) of the important VOCs for different patient populations.

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    Summary statistic(s) of the important VOCs for different patient populations.</p

    Identification and characterization of key common VOCs for the patients’ classification under all the five cases.

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    (A) Venn diagram for the significant VOCs identified through the Boot-SVM-RFE technique. Significant VOCs are identified by setting the threshold for p-values at 10−15. Through this, 15, 15, 15, 13 and 13 significant VOCs are selected for the Case I, Case II, Case III, Case IV, and Case V, respectively. Case I: Lung cancer vs. Control; Case II: Cancer vs. Benign; Case III: Benign vs. Control; Case IV: (Benign + Cancer) vs. Control; Case V: (Control + Benign) vs. Cancer. (B) Characterization of the common significant VOCs. (C) Summary statistics of the seven significant VOCs with mean and median concentrations (nmole/L) over the whole sample (n = 414). SD: Standard deviation; SE: Standard error; CI: Confidence Interval.</p

    Similarity and correlation among the VOCs.

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    (A) Dendrogram plot for the VOCs. A dendrogram is a diagram that shows the hierarchical relationship between the VOCs and is obtained through hierarchical clustering method. (B) Correlation plot for the VOCs. The correlation values with white spots represent non-significant correlation among the VOCs at 5% level of significance.</p
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