Wavelet-based gene selection method for survival prediction in diffuse large B-cell lymphomas patients

IF 0.2 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Data Mining and Bioinformatics Pub Date : 2015-08-01 DOI:10.1504/IJDMB.2015.071556

M. Farhadian, H. Mahjub, A. Moghimbeigi, P. Lisboa, J. Poorolajal, Muharram Mansoorizadeh

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引用次数: 1

Abstract

Microarray technology allows simultaneous measurements of expression levels for thousands of genes. An important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on wavelet transform for survival-relevant gene selection is presented. Cox proportional hazard model is typically used to build prediction model for patients' survival using the selected genes. The prediction model will be evaluated with the R2, concordance index, likelihood ratio statistic and Akaike information criteria. The results proved that good performance of survival prediction is achieved based on the selected genes. The results suggested the possibility of developing more advanced tools based on wavelets for gene selection from microarray data sets in the context of survival analysis.

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基于小波的基因选择方法预测弥漫性大b细胞淋巴瘤患者的生存

微阵列技术允许同时测量数千个基因的表达水平。微阵列研究的一个重要方面包括基于基因表达谱预测患者生存。这自然需要使用降维程序和生存预测模型。本文提出了一种基于小波变换的生存相关基因选择新方法。通常采用Cox比例风险模型，利用所选基因建立患者生存预测模型。采用R2、一致性指数、似然比统计量和赤池信息准则对预测模型进行评价。结果表明，基于所选基因的生存预测取得了较好的效果。结果表明，在生存分析的背景下，基于小波的基因选择微阵列数据集开发更先进的工具的可能性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Data Mining and Bioinformatics 生物-数学与计算生物学

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.