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Improving cancer classification accuracy using gene pairs

Pankaj Chopra1, Jinseung Lee, Jaewoo Kang

  • 1Department of Human Genetics, School of Medicine, Emory University, Atlanta, Georgia, United States of America.

Plos One
|January 5, 2011
PubMed

Insights

Cancer biomarkers are improved by using gene pair combinations (doublets) instead of single genes. This approach enhances cancer classification accuracy across multiple datasets and algorithms, offering a more robust diagnostic strategy.

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Carcinogenesis may be triggered by pathway deregulation rather than individual genes.
  • Pathway deregulation often results from the simultaneous dysregulation of multiple genes within a pathway.
  • Gene pair combinations (doublets) may better capture underlying biomolecular reactions relevant to pathway deregulation.

Purpose of the Study:

  • To investigate if gene pair combinations (doublets) improve cancer classification accuracy compared to individual genes.
  • To validate the hypothesis that robust gene pair combinations serve as superior cancer biomarkers.

Main Methods:

  • Utilized gene pair combinations (doublets) as input for cancer classification algorithms.
  • Compared classification accuracy using doublets versus original gene expression values.
  • Validated the approach across nine cancer datasets and five classification algorithms: Prediction Analysis for Microarrays (PAM), C4.5 Decision Trees (DT), Naive Bayesian (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN).

Main Results:

  • Consistently improved classification accuracy across different datasets when using gene pair combinations.
  • Demonstrated the effectiveness of doublets as input for various classification algorithms.
  • The proposed approach showed enhanced performance in cancer classification tasks.

Conclusions:

  • Gene pair combinations (doublets) offer a more robust approach for cancer biomarker discovery and classification.
  • Utilizing doublets significantly enhances the accuracy of cancer classification algorithms.
  • This method provides a promising avenue for improving diagnostic strategies in oncology.

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