The painter's feature selection for gene expression data.

Daniele Apiletti, Elena Baralis, Giulia Bruno, Alessandro Fiori

Research output: Contribution to journalArticle

Abstract

Feature selection is a fundamental task in microarray data analysis. It aims at identifying the genes which are mostly associated with a tissue category, disease state or clinical outcome. An effective feature selection reduces computation costs and increases classification accuracy. This paper presents a novel multi-class approach to feature selection for gene expression data, which is called Painter's approach. It has the benefits of both a parameter free technique and a native multicategory method. It consists of two phases. The first is a filtering phase that smooths the effect of noise and outliers, which represent a common problem in microarray data. In the second phase, the actual gene selection is performed. Preliminary experimental results on three public datasets are presented. They confirm the intuition of the proposed approach leading to high classification accuracies.

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Gene expression
Feature extraction
Microarrays
Gene Expression
Intuition
Microarray Analysis
Genes
Noise
Costs and Cost Analysis
Tissue
Costs
Datasets

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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abstract = "Feature selection is a fundamental task in microarray data analysis. It aims at identifying the genes which are mostly associated with a tissue category, disease state or clinical outcome. An effective feature selection reduces computation costs and increases classification accuracy. This paper presents a novel multi-class approach to feature selection for gene expression data, which is called Painter's approach. It has the benefits of both a parameter free technique and a native multicategory method. It consists of two phases. The first is a filtering phase that smooths the effect of noise and outliers, which represent a common problem in microarray data. In the second phase, the actual gene selection is performed. Preliminary experimental results on three public datasets are presented. They confirm the intuition of the proposed approach leading to high classification accuracies.",
author = "Daniele Apiletti and Elena Baralis and Giulia Bruno and Alessandro Fiori",
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language = "English",
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AB - Feature selection is a fundamental task in microarray data analysis. It aims at identifying the genes which are mostly associated with a tissue category, disease state or clinical outcome. An effective feature selection reduces computation costs and increases classification accuracy. This paper presents a novel multi-class approach to feature selection for gene expression data, which is called Painter's approach. It has the benefits of both a parameter free technique and a native multicategory method. It consists of two phases. The first is a filtering phase that smooths the effect of noise and outliers, which represent a common problem in microarray data. In the second phase, the actual gene selection is performed. Preliminary experimental results on three public datasets are presented. They confirm the intuition of the proposed approach leading to high classification accuracies.

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