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Related Experiment Video

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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

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Published on: February 25, 2017

A new measure of classifier performance for gene expression data.

Blaise Hanczar¹, Avner Bar-Hen

¹LIPADE, University Paris Descartes, Paris, France. hanczar_blaise@yahoo.fr

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|February 1, 2012

Summary

This study introduces a novel classifier performance measure for microarray data, accounting for uncertain error costs. It improves classifier selection when traditional methods fail, especially in diagnostics.

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Area of Science:

Bioinformatics
Computational Biology
Biostatistics

Background:

Microarray experiments aim to develop diagnostic and prognostic models.
Supervised classification methods are widely used for microarray data analysis.
Evaluating and comparing classifiers based on classification cost is critical but challenging due to unknown false positive/negative costs.

Purpose of the Study:

To propose a new measure for classifier performance that addresses uncertainty in error costs.
To develop a method that accounts for unknown cost ratios in diagnostic problems.
To improve the reliability of classifier evaluation and comparison in microarray studies.

Main Methods:

Developed a novel performance measure incorporating uncertainty of classification costs.
Represented cost knowledge using a probability distribution function on the cost ratio.
Computed classifier performance weighted by the probability distribution of all possible costs.

Main Results:

The proposed method was tested on artificial and real microarray datasets.
Classifier performance was shown to be highly dependent on the ratio of classification costs.
The new measure identified optimal classifiers where classic error measures failed.

Conclusions:

The proposed performance measure effectively handles uncertainty in error costs for microarray classification.
This approach offers a more robust method for evaluating and comparing classifiers in diagnostic applications.
The findings highlight the limitations of traditional error measures when cost information is uncertain.