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Related Experiment Videos

On the statistical assessment of classifiers using DNA microarray data.

N Ancona1, R Maglietta, A Piepoli

  • 1lstituto di Studi sui Sistemi Intelligenti per I'Automazione-CNR, Via Amendola 122/D-l, 70126 Bari, Italy. ancona@ba.issia.cnr.it

BMC Bioinformatics
|August 22, 2006
PubMed
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Accurate colon cancer classification is achievable with minimal data. Statistical methods assess gene expression profiles, identifying key genes for reliable diagnosis and prognosis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Presents a novel statistical method for assessing cancer predictors using gene expression profiles.
  • Utilizes a new dataset of microarray gene expression data from colon cancer patients (22 normal, 25 tumor specimens).

Purpose of the Study:

  • To determine if available data is sufficient for accurate cancer classifiers.
  • To assess the statistical significance of error rates in classification.
  • To identify the number of genes crucial for accurate colon cancer classification.

Main Methods:

  • Employs Leave-K-Out Cross Validation to estimate generalization error for three classification schemes.
  • Uses permutation tests to measure the statistical significance of error rates.

Related Experiment Videos

  • Analyzes gene frequencies and their correlation with colon cancer pathology.

    • Main Results:

    • Weighted Voting Algorithm (WVA) achieved 21% error with 25 training examples (p=0.045).
    • Regularized Least Squares (RLS) and Support Vector Machines (SVM) achieved 19% (p=0.035) and 18% (p=0.037) error with 15 training examples, respectively.
    • RLS and SVM reached optimal performance with 35 training examples (14% and 11% error, respectively) and identified ~6000 significant genes (p<0.05).

    Conclusions:

    • The proposed method offers statistically significant insights for cancer diagnosis and prognosis.
    • Effective colon cancer classifiers can be trained with as few as 15 examples.
    • The number of genes required for reliable classification is dependent on the desired accuracy.