Bicoid Signal Extraction with a Selection of Parametric and Nonparametric Signal Processing Techniques

作     者：Zara Ghodsi Emmanuel Sirimal Silva Hossein Hassani 

作者机构：The Statistical Research CentreBournemouth University Institute for International Energy Studies (ⅡES) 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2015年第13卷第3期

页      面：183-191页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 1001[医学-基础医学(可授医学、理学学位)] 071008[理学-发育生物学] 0714[理学-统计学（可授理学、经济学学位）] 0703[理学-化学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Theauthorswouldliketoexpresstheirsinceregratitudetotheeditorandthethreeanonymousrefereesfortheirhighlycon-structivefeedbackandcomments 

主　　题：Bicoid Drosophila melanogaster Signal extraction Signal processing 

摘      要：The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraetion in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.