ClinProTools™ (CPT)

Category Cross-Omics>Biomarker Discovery/Analysis/Tools

Abstract ClinProTools™ (CPT) provides an advanced basis for mining potential biomarkers in complex protein profiles.

Its visualization tools can be used for the interactive inspection and comparison of large data sets originating from samples that contain different clinical diagnosis.

Additionally, the ClinProTools software supplies highly sophisticated 'mathematical algorithms' for the discovery of complex biomarker pattern models.

Besides validation, 'class-prediction' tools are also integrated to allow the implementation of the whole biomarker detection and evaluation process.

ClinProTools completes the ClinProt™ workflow, allowing access to mass spectrometry data for rapid and comprehensive evaluation and to mine potential biomarkers in complex protein profiles.

Products features/capabilities include:

ClinProTools supports a typical biomarker discovery project workflow:

1) Analysis of training data sets to generate the best biomarker pattern model.

2) Cross validation and/or validation of models by an independent data set.

3) Classification of ‘patient sets’ for class prediction.

Concept of ClinProTools --

1) Visualization of large numbers of data sets with intuitive tools: Spectra view, virtual gel view, stack plots, contour plot, cross-section view, box-and-whiskers plot.

2) Creation of predictive biomarker pattern models through multivariate bioinformatics tools by employing Genetic Algorithms and Support Vector Machines (SVM).

3) Validation of the models by external samples with determination of specificity and sensitivity.

4) Classification of unknown samples.

Visualization of Data -

1) Display of single and average spectra to allow inspection of data.

2) A gel view display of all spectra at a particular time.

3) Intuitive display functions.

4) Visualization of peak info from each sample for display of the best separating peaks.

Model generation: Univariate and multivariate statistical tools --

ClinProTools 2.0 offers both statistical tests for expected normally distributed data as well as for data sets that are Not normally distributed.

As an output, the software creates a table of peaks that can be sorted according to its lowest p-value. The p-value describes the significance and probability of single peaks in order to separate the classes.

The output is designed to give the user a maximum level of convenience together with high flexibility. All algorithms used for model generation support multiple classes.

Single peaks statistics with a quick univariate sorting algorithm -- For the statistics of single peaks, CPT 2.0 uses the Quick Classifier algorithm (QC) which is a univariate sorting algorithm.

Univariate analysis can be very useful for discovering statistically significant biomarker candidates.

Two (2) advanced multivariate analysis tools are part of ClinProTools 2.0 --

1) Genetic Algorithms:

Genetic Algorithms are inspired by the theory of evolution. These algorithms mimic the evolutionary process by finding the fittest solution from multiple models. Here, a model is defined as combination of peaks.

These models undergo - in analogy to genetics - processes like chromosomal cross-over, mutation, and a selection of the fittest result. As a result, a new generation of models will be created which will again undergo selection.

After multiple generations (the number can be defined in the software) the fitness will remain stable and the algorithm stops.

2. Support Vector Machine:

The Support Vector Machine (SVM) is historically a classifier and Not a feature selection algorithm. SVM tries to find a hyper-plane that separates one or more classes.

In the simplest case, the SVM helps to determine an optimal hyper- plane separating two clouds of data. The algorithm tries to find this line in a multi-dimensional space.

Validation and class prediction -- Validation of the models can be achieved by using an independent training data set. As a result, the user will get the 'sensitivity and specificity' of the model as 'percentage of correctly classified' data.

If only a limited set of data is available, 'cross validation' may be used which means that one or more spectra are taken out of the training set and used for clustering.

ClinProTools 2.1 - currently available -- The new version of this biomarker profiling solution includes additional features for data analysis and visualization.

Especially, this software allows the researcher to merge statistical analysis of profiling spectra with molecular images of tissue distributions of biomarkers from the MALDI Molecular Imager™ system.

New features of CPT 2.1:

1) Supervised Neural Network™ algorithm (SNN) - Bruker Daltonics developed the SNN algorithm, an approach based on a widely accepted prototype based classifier with a high generalization ability. It is specially suited for high dimensional multi-modal data.

2) Principle Component Analysis (PCA) - The PCA is an unsupervised data analysis approach which allows a visual inspection of the data distribution.

3) Receiver Operating Characteristics (ROC) - The per-peak ROC curve gives a visual overview of the class separating capability of single peaks acc. specificity and sensitivity.

4) Quality control functionality - Data quality control is supported by the help of the PCA and the 2D Peak Distribution views allowing you to evaluate the data quality by analyzing class internal variations.

5) Working together with MALDI Molecular Imager - In combination with the new flexImaging 2.0 software, ClinProTools 2.1 is one of the first commercially available bioinformatics packages which merge statistical analysis of MALDI-TOF mass spectrometry-based profiling data of samples from different classes with imaging of tissue distribution of peptide and protein biomarkers using the MALDI Molecular Imager.

System Requirements

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Manufacturer Web Site ClinProTools (CPT)

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G6G Abstract Number 20241

G6G Manufacturer Number 100442