15 research outputs found
MOESM6 of Freeze-drying: an alternative method for the analysis of volatile organic compounds in the headspace of urine samples using solid phase micro-extraction coupled to gas chromatography - mass spectrometry
Additional file 6. Column degradation. This csv file contains the metabolites defined as product of GC column degradation. This file can be visualised using a text editor
MOESM4 of Freeze-drying: an alternative method for the analysis of volatile organic compounds in the headspace of urine samples using solid phase micro-extraction coupled to gas chromatography - mass spectrometry
Additional file 4. AMDIS configuration. This ini file contains the settings of the AMDIS software used in this study. Please see the user manual provided with AMDIS to obtain all the necessary information to use this configuration file when analysing GC-MS samples with AMDIS
MOESM5 of Freeze-drying: an alternative method for the analysis of volatile organic compounds in the headspace of urine samples using solid phase micro-extraction coupled to gas chromatography - mass spectrometry
Additional file 5. Metabolite found in urine samples. This csv file contains the metabolites found in the samples analysed in this study. This file can be visualised using a text editor
Examples of chromatograms (standardised resistance vs. time) of the urine of two patients with bladder cancer and two controls.
<p>Examples of chromatograms (standardised resistance vs. time) of the urine of two patients with bladder cancer and two controls.</p
The retention time stability of three common peaks from 24 selected control samples and the 24 bladder cancer samples, in date order.
<p>There is a minimal day to day fluctuation <i>circa</i> ± 1%.</p
The averaged chromatograms for the bladder cancer group and the control group shown on the same standardised resistance time plot.
<p>The averaged chromatograms for the bladder cancer group and the control group shown on the same standardised resistance time plot.</p
Accuracy results of PSA testing for prostate cancer assessed using repeated double cross validation of Random Forest and Linear Discriminant Analysis (LDA) models.
<p>Accuracy results of PSA testing for prostate cancer assessed using repeated double cross validation of Random Forest and Linear Discriminant Analysis (LDA) models.</p
Accuracy results of PSA testing for prostate cancer assessed with repeated 10 fold cross validation of Random Forest and Linear Discriminant Analysis (LDA) models.
<p>Accuracy results of PSA testing for prostate cancer assessed with repeated 10 fold cross validation of Random Forest and Linear Discriminant Analysis (LDA) models.</p
The pipeline of the validation techniques known as repeated 10 fold cross validation and repeated double cross validation.
<p>A Monte-Carlo variation of each technique is achieved by randomising the labels of the testing samples.</p
Accuracy results of repeated 10 fold cross validation of the Random Forest and Linear Discriminant Analysis (LDA) models built to classify urine samples from patients with prostate cancer and cancer-free controls based on the presence or absence of VOCs.
<p>Accuracy results of repeated 10 fold cross validation of the Random Forest and Linear Discriminant Analysis (LDA) models built to classify urine samples from patients with prostate cancer and cancer-free controls based on the presence or absence of VOCs.</p