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Related Concept Videos

Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Comprehensive feature analysis for sample classification with comprehensive two-dimensional LC.

Stephen E Reichenbach1, Xue Tian, Qingping Tao

  • 1Computer Science and Engineering Department, University of Nebraska-Lincoln, Lincoln, NE 68588-0115, USA. reich@cse.unl.edu

Journal of Separation Science
|April 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing complex liquid chromatography x liquid chromatography (LC x LC) data to classify biological samples. The approach effectively extracts features for accurate sample classification, aiding biomarker discovery.

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Published on: September 2, 2020

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Last Updated: Jun 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

Area of Science:

  • Analytical Chemistry
  • Biomarker Discovery
  • Chemometrics

Background:

  • Comprehensive two-dimensional LC (LC x LC) offers powerful separation for complex biological samples.
  • LC x LC generates rich chromatograms requiring advanced data analysis for biomarker extraction.
  • Classifying biological samples based on chromatographic features is crucial for health condition assessment.

Purpose of the Study:

  • To present a novel approach for extracting comprehensive, non-targeted chromatographic features from LC x LC data.
  • To utilize these extracted features for effective sample classification.
  • To demonstrate the utility of the method in analyzing complex biological samples like urine.

Main Methods:

  • Development of a new data analysis approach for extracting comprehensive chromatographic features from LC x LC data.
  • Application of a support vector machine (SVM) for sample classification.
  • Utilizing leave-one-out and replicate K-fold cross-validation for performance assessment.

Main Results:

  • The new approach successfully extracted relevant chromatographic features from LC x LC chromatograms.
  • Extracted features enabled effective classification of urine samples.
  • SVM achieved high accuracy in classifying samples by individual, pre/post-procedure status, and concentration.

Conclusions:

  • The developed method provides a powerful tool for comprehensive chromatographic feature analysis in LC x LC separations.
  • This approach facilitates the classification of complex biological samples, supporting biomarker identification.
  • The findings suggest significant potential for this method in clinical and research applications.