Fourier harmonic approach for visualizing temporal patterns of gene expression data

Zhang, L.; Zhang, A.; Ramanathan, Murali

doi:10.1109/csb.2003.1227313

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Smoothing away noise-induced wiggles with Fourier series has been studied by some researchers. Zhang et al (2003) used the first harmonic of discrete Fourier transform to translate the multi-dimensional time series microarray expression data into a two-dimensional scatter plot. Murthy and Hua (2004) proposed improved Fourier method considering irregular or monotonic component of cell-cycle expression.…”

Section: Introductionmentioning

confidence: 99%

Clustering of change patterns using Fourier coefficients

Kim

2007

Bioinformatics

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Section: Introductionmentioning

confidence: 99%

Clustering of change patterns using Fourier coefficients

Kim

2007

Bioinformatics

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“…In the literature, other methods have been developed and studied to analyze gene expression time series data, see for instance [18][19][20][21][22][23][24][25][26][27][28][29][30]. However, their approaches are different from our proposed PCA-NN modeling for gene expression time series.…”

mentioning

confidence: 97%

“…The visualizing of the gene expression time series is discussed in studies [28,29]. Zhang et al [28] have introduced the first Fourier harmonic projection (FFHP) to translate the multi-dimensional time series data into a two-dimensional scatter plot.…”

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confidence: 99%

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Gene expression time series modeling with principal component and neural network

2005

Soft Comput

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In this work, gene expression time series models have been constructed by using principal component analysis (PCA) and neural network (NN). The main contribution of this paper is to develop a methodology for modeling numerical gene expression time series. The PCA-NN prediction models are compared with other popular continuous prediction methods. The proposed model can give us the extracted features from the gene expressions time series and the orders of the prediction accuracies. Therefore, the model can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. Based on the results of two public real datasets, the PCA-NN method outperforms the other continuous prediction methods. In the time series model, we adapt Akaike's information criteria (AIC) tests and cross-validation to select a suitable NN model to avoid the overparameterized problem.

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“…Zhang et al (2003Zhang et al ( , 2004 have introduced a Fourier transform based method for representing and analyzing multidimensional datasets. Mapping of multidimensional data into a 2-D plot is achieved by identifying the first harmonic of the Fourier projection.…”

Section: Visualization Of Multi-dimensional Datasetsmentioning

confidence: 99%

Use of Wavelet and Fast Fourier Transforms in Pharmacodynamics

Mager¹,

Abernethy²

2006

J Pharmacol Exp Ther

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Progress has been made in the development and application of mechanism-based pharmacodynamic models for describing the drug-specific and physiological factors influencing the time course of responses to the diverse actions of drugs. However, the biological variability in biosignals and the complexity of pharmacological systems often complicate or preclude the direct application of traditional structural and nonstructural models. Mathematical transforms may be used to provide measures of drug effects, identify structural and temporal patterns, and visualize multidimensional data from analyses of biomedical signals and images. Fast Fourier transform (FFT) and wavelet analyses are two methodologies that have proven to be useful in this context. FFT converts a signal from the time domain to the frequency domain, whereas wavelet transforms colocalize in both domains and may be utilized effectively for nonstationary signals. Nonstationary drug effects are common but have not been well analyzed and characterized by other methods. In this review, we discuss specific applications of these transforms in pharmacodynamics and their potential role in ascertaining the dynamics of spatiotemporal properties of complex pharmacological systems.Quantitative pharmacology involves the characterization of drug effects within and across scales of organization by means of integrating drug-specific properties and those reflective of physiological processes and control systems. A major goal is to identify specific factors that influence the time course of pharmacological effects; however, biosignals emanating from complex physiological systems often exhibit temporal variability. As opposed to simply errors in measurement assay, time-series analysis has revealed that such highly variable data are a product of nonlinear dynamical systems that can be described by chaos theory (Tallarida, 1990;van Rossum and de Bie, 1991; Dokoumetzidis et al., 2001;Goldberger et al., 2002). Traditional pharmacodynamic models are unable to recognize such complex and variable information.High-frequency measurement of drug effects allows development of concepts of pharmacodynamic systems analysis and drug effects based on integrated signaling networks. There are many challenges to this approach; however, recognition of the basic tenets of the complexity, robustness, emergent properties, and intrinsic noise of biological signaling networks provide insight for their analysis (Weng et al., 1999;Aderem, 2005). Current approaches attempting to characterize such interactions in mechanistic terms include deterministic systems such as ordinary differential equations (e.g., chemical kinetics and compartmental models) and partial differential equations (e.g., reaction-diffusion models), stochastic systems (frequently used for species existing in small numbers), and hybrid systems that combine deterministic and stochastic components (Eungdamrong and Iyengar, 2004).

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Fourier harmonic approach for visualizing temporal patterns of gene expression data

Cited by 9 publications

References 22 publications

Clustering of change patterns using Fourier coefficients

Clustering of change patterns using Fourier coefficients

Gene expression time series modeling with principal component and neural network

Use of Wavelet and Fast Fourier Transforms in Pharmacodynamics

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