A Gene Selection Method for Survival Prediction in Diffuse Large B-Cell Lymphomas Patients using 1D Discrete Wavelet Transform.

Maryam Farhadian, Hossein Mahjub, Abbas Moghimbeigi, Jalal Poorolajal, Muharram Mansoorizadeh

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Article in Iranian journal of public health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests.Iranian journal of public health · 2016
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5 authors.

Maryam Farhadian1. Dept. of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences , Hamadan. Iran .

Hossein Mahjub2. Research Center for Health Sciences and Dept. of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences , Hamadan, Iran.

Abbas Moghimbeigi3. Modeling of Noncommunicable Diseases Research Center and Dept. of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences , Hamadan, Iran.

Jalal Poorolajal3. Modeling of Noncommunicable Diseases Research Center and Dept. of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences , Hamadan, Iran.

Muharram Mansoorizadeh4. Dept. of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University , Hamadan, Iran.

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No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. To deal with the high dimensionality of this data, use of a dimension reduction procedure along with the survival prediction model is necessary. This study aimed to present a new method based on wavelet transform for survival relevant gene selection.

methodsThe data included 2042 gene expression measurements from 40 patients with Diffuse Large B-Cell Lymphomas (DLBCL). The pre-processing gene expression data is decomposed using third level of the 1D discrete wavelet transform. The detail coefficients at levels 1 and 2 are filtered out and expression data reconstructed using the approximation and detailed coefficients at the third level. All the genes are then scored based on the t score. Then genes with the highest scores are selected. By using forward selection method in Cox regression model, significant genes were identified.

resultsThe results showed wavelet-based gene selection method presents acceptable survival prediction. Using this method, six significant genes were selected. It was indicated the expression of GENE3359X and GENE3968X decreased the survival time, whereas the expression of GENE967X, GENE3980X, GENE3405X and GENE1813X increased the survival time.

conclusionWavelet-based gene selection method is a potentially useful tool for the gene selection from microarray data in the context of survival analysis.

Indexed as

DLBCLMicroarray dataOne dimensional wavelet transformSurvival analysis

Identifiers

PMID25927038

PMCPMC4411905

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