Bicoid signal extraction with a selection of parametric and nonparametric signal processing techniques.

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 extraction 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

Optimizing bicoid signal extraction.

Signal extraction and analysis is of great importance, not only in fields such as economics and meteorology, but also in genetics and even biomedicine. There exists a range of parametric and nonparametric techniques which can perform signal extractions. However, the aim of this paper is to define a new approach for optimising signal extraction from bicoid gene expression profile. Having studied both parametric and nonparametric signal extraction techniques, we identified the lack of specific criteria enabling users to select the optimal signal extraction parameters. Exploiting the expression profile of bicoid gene, which is a maternal segmentation coordinate gene found in Drosophila melanogaster, we introduce a new approach for optimising the signal extraction using a nonparametric technique. The underlying criteria are based on the distribution of the residual, more specifically its skewness

A novel statistical signal processing approach for analysing high volatile expression profiles.

The aim of this research is to introduce new advanced statistical methods for analysing gene expression profiles to consequently enhance our understanding of the spatial gradients of the proteins produced by genes in a gene regulatory network (GRN). To that end, this research has three main contributions. In this thesis, the segmentation Network (SN) in Drosophila melanogaster and the bicoid gene (bcd) as the critical input of this network are targeted to study. The first contribution of this research is to introduce a new noise filtering and signal processing algorithm based on Singular Spectrum Analysis (SSA) for extracting the signal of bicoid gene. Using the proposed SSA algorithm which is based on the minimum variance estimator, the extraction of bcd signal from its noisy profile is considerably improved compared to the most widely accepted model, Synthesis Diffusion Degradation (SDD). The achieved results are evaluated via both simulation studies and empirical results. Given the reliance of this research towards introducing an improved signal extraction approach, it is mandatory to compare the proposed method with the other well-known and widely used signal processing models. Therefore, the results are compared with a range of parametric and non-parametric signal processing methods. The conducted comparison study confirmed the outperformance of the SSA technique. Having the superior performance of SSA, in the second contribution, the SSA signal extraction performance is optimised using several novel computational methods including window length and eigenvalue identification approaches, Sequential and Hybrid SSA and SSA based on Colonial Theory. Each introduced method successfully improves a particular aspect of the SSA signal extraction procedure. The third and final contribution of this research aims at extracting the regulatory role of the maternal effect genes in SN using a variety of causality detection techniques. The hybrid algorithm developed here successfully portrays the interactions which have been previously accredited via laboratory experiments and therefore, suggests a new analytical view to the GRNs

Estimation of protein diffusion parameters

Protein diffusion offers an essential and elegant mechanism for morphogen gradient formation. Morphogens are signalling molecules that emanate from a particular region of the cell and create a gradient which has an impact on most biological processes, cell signalling and embryonic development. Using a method that is based on Singular Spectrum Analysis, we estimate parameters introduced in the Synthesis Diffusion Degradation model which is a commonly applied model for a transcription factor known as Bicoid. Our findings, consistent with simulation results, indicate that the proposed method can be practically applied as an enhanced parameter estimation technique with reduced sensitivity to various levels of noise

Noise correction in gene expression data: a new approach based on subspace method

Copyright © 2016 John Wiley & Sons, Ltd. We present a new approach for removing the nonspecific noise from Drosophila segmentation genes. The algorithm used for filtering here is an enhanced version of singular spectrum analysis method, which decomposes a gene profile into the sum of a signal and noise. Because the main issue in extracting signal using singular spectrum analysis procedure lies in identifying the number of eigenvalues needed for signal reconstruction, this paper seeks to explore the applicability of the new proposed method for eigenvalues identification in four different gene expression profiles. Our findings indicate that when extracting signal from different genes, for optimised signal and noise separation, different number of eigenvalues need to be chosen for each gene. Copyright © 2016 John Wiley & Sons, Ltd

A New Signal Processing Approach for Discrimination of EEG Recordings

Classifying brain activities based on electroencephalogram(EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper,we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are compared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further

Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network

Forecasting tourism demand with denoised neural networks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The automated Neural Network Autoregressive (NNAR) algorithm from the forecast package in R generates sub-optimal forecasts when faced with seasonal tourism demand data. We propose denoising as a means of improving the accuracy of NNAR forecasts via an application into forecasting monthly tourism demand for ten European countries. Initially, we fit NNAR models on both raw and denoised (with Singular Spectrum Analysis) tourism demand series, generate forecasts and compare the results. Thereafter, the denoised NNAR forecasts are also compared with parametric and nonparametric benchmark forecasting models. Contrary to the deseasonalising hypothesis, we find statistically significant evidence which supports the denoising hypothesis for improving the accuracy of NNAR forecasts. Thus, it is noise and not seasonality which hinders NNAR forecasting capabilities