Article

A method of tumor classification based on wavelet packet transforms and neighborhood rough set.

Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China.
Computers in biology and medicine (impact factor: 1.27). 03/2010; 40(4):430-7. DOI:10.1016/j.compbiomed.2010.02.007 pp.430-7
Source: PubMed

ABSTRACT Tumor classification is an important application domain of gene expression data. Because of its characteristics of high dimensionality and small sample size (SSS), and a great number of redundant genes not related to tumor phenotypes, various feature extraction or gene selection methods have been applied to gene expression data analysis. Wavelet packet transforms (WPT) and neighborhood rough sets (NRS) are effective tools to extract and select features. In this paper, a novel approach of tumor classification is proposed based on WPT and NRS. First the classification features are extracted by WPT and the decision tables are formed, then the attributes of the decision tables are reduced by NRS. Thirdly, a feature subset with few attributes and high classification ability is obtained. The experimental results on three gene expression datasets demonstrate that the proposed method is effective and feasible.

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    Article: Multifocal ERG wavelet packet decomposition applied to glaucoma diagnosis.
    Juan M Miguel-Jiménez, Sergio Ortega, Luciano Boquete, José M Rodríguez-Ascariz, Román Blanco
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    ABSTRACT: Glaucoma is the second-leading cause of blindness worldwide and early diagnosis is essential to its treatment. Current clinical methods based on multifocal electroretinography (mfERG) essentially involve measurement of amplitudes and latencies and assume standard signal morphology. This paper presents a new method based on wavelet packet analysis of global-flash multifocal electroretinogram signals. This study comprised twenty-five patients diagnosed with OAG and twenty-five control subjects. Their mfERG recordings data were used to develop the algorithm method based on wavelet packet analysis. By reconstructing the third wavelet packet contained in the fourth decomposition level (ADAA4) of the mfERG recording, it is possible to obtain a signal from which to extract a marker in the 60-80 ms time interval. The marker found comprises oscillatory potentials with a negative-slope basal line in the case of glaucomatous recordings and a positive-slope basal line in the case of normal signals. Application of the optimal threshold calculated in the validation cases showed that the technique proposed achieved a sensitivity of 0.81 and validation specificity of 0.73. This new method based on mfERG analysis may be reliable enough to detect functional deficits that are not apparent using current automated perimetry tests. As new stimulation and analysis protocols develop, mfERG has the potential to become a useful tool in early detection of glaucoma-related functional deficits.
    BioMedical Engineering OnLine 01/2011; 10:37. · 1.40 Impact Factor
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Keywords

attributes
 
characteristics
 
classification features
 
decision tables
 
experimental results
 
feature subset
 
features
 
gene expression data analysis
 
gene expression datasets
 
gene selection methods
 
great number
 
neighborhood rough sets
 
proposed method
 
redundant genes
 
small sample size
 
Thirdly
 
Tumor classification
 
tumor phenotypes
 
various feature extraction
 
WPT
 

Shan-Wen Zhang