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Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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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Cancer Survival Analysis

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Updated: Jun 5, 2026

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

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Published on: October 11, 2018

PUGSVM: a caBIG™ analytical tool for multiclass gene selection and predictive classification.

Guoqiang Yu1, Huai Li, Sook Ha

  • 1Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.

Bioinformatics (Oxford, England)
|December 28, 2010
PubMed
Summary

Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a novel tool for cancer gene selection and classification. This method enhances accuracy in identifying disease markers, even with complex datasets.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Cancer Research

Background:

  • The Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is an analytical tool developed within the cancer Biomedical Informatics Grid (caBIG™).
  • It is designed to address challenges in multiclass gene selection and classification, including imbalanced datasets, small sample sizes, and high dimensionality.
  • The tool utilizes a one-versus-rest support vector machine approach.

Purpose of the Study:

  • To introduce and evaluate the PUGSVM tool for multiclass gene selection and classification in cancer research.
  • To provide a more accurate strategy for identifying statistically reproducible mechanistic marker genes.
  • To aid in the characterization of heterogeneous diseases.

Main Methods:

  • PUGSVM defines multiclass gene markers as the union of one-versus-everyone phenotypic upregulated genes.
  • It employs a well-matched one-versus-rest support vector machine for classification.
  • The approach is designed to handle imbalanced class separability and high gene space dimensionality.

Main Results:

  • PUGSVM offers a simple yet accurate strategy for gene selection and classification.
  • The tool facilitates the identification of statistically reproducible mechanistic marker genes.
  • It shows promise in characterizing heterogeneous diseases.

Conclusions:

  • PUGSVM represents a significant advancement in cancer bioinformatics tools.
  • The method provides a robust approach for identifying disease-specific gene markers.
  • PUGSVM can improve the understanding and classification of complex diseases.