Modelling gene expression time-series with radial basis function neural networks

The global nature of the Czech economy means that quantitative knowledge of the influence of the exchange rate provides useful information for all participants in the international economy. Systematic and academic research show that the issue of estimating the Czech crown/Chinese yuan exchange rate, with consideration for seasonal fluctuations, has yet to be dealt with in detail. The aim of this contribution is to present a methodology based on neural networks that takes into consideration seasonal fluctuations when equalizing time series by using the Czech crown and Chinese yuan as examples. The analysis was conducted using daily information on the Czech crown/Chinese yuan exchange rate over a period of more than nine years. This is the equivalent of 3303 data inputs. Statistica software, version 12 by Dell Inc. was used to process the input data and, subsequently, to generate multi-layer perceptron networks and radial basis function neural networks. Two versions of neural structures were produced for regression purposes, the second of which used seasonal fluctuations as a categorical variable–year, month, day of the month and week—when the value was measured. All the generated and retained networks had the ability to equalize the analyzed time series, although the second variant demonstrated higher efficiency. The results indicate that additional variables help the equalized time series to retain order and precision. Of further interest is the finding that multi-layer perceptron networks are more efficient than radial basis function neural networks.

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On the prediction of chaotic time series using a new generalized radial basis function neural networks

Acta Physica Sinica ◽

10.7498/aps.54.4569 ◽

2005 ◽

Vol 54 (10) ◽

pp. 4569

Author(s):

Li Jun ◽

Liu Jun-Hua

Keyword(s):

Neural Networks ◽

Time Series ◽

Radial Basis Function ◽

Basis Function ◽

Chaotic Time Series ◽

Radial Basis

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Inference of gene networks from gene expression time series using recurrent neural networks and sparse MAP estimation

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720018500099 ◽

2018 ◽

Vol 16 (04) ◽

pp. 1850009

Author(s):

Chi-Kan Chen

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Time Series ◽

Recurrent Neural Networks ◽

Genetic Regulatory Networks ◽

Small Scale ◽

Map Estimation ◽

Inference Algorithms ◽

Gene Expression Time Series ◽

Expression Time

Background: The inference of genetic regulatory networks (GRNs) provides insight into the cellular responses to signals. A class of recurrent neural networks (RNNs) capturing the dynamics of GRN has been used as a basis for inferring small-scale GRNs from gene expression time series. The Bayesian framework facilitates incorporating the hypothesis of GRN into the model estimation to improve the accuracy of GRN inference. Results: We present new methods for inferring small-scale GRNs based on RNNs. The weights of wires of RNN represent the strengths of gene-to-gene regulatory interactions. We use a class of automatic relevance determination (ARD) priors to enforce the sparsity in the maximum a posteriori (MAP) estimates of wire weights of RNN. A particle swarm optimization (PSO) is integrated as an optimization engine into the MAP estimation process. Likely networks of genes generated based on estimated wire weights are combined using the majority rule to determine a final estimated GRN. As an alternative, a class of [Formula: see text]-norm ([Formula: see text]) priors is used for attaining the sparse MAP estimates of wire weights of RNN. We also infer the GRN using the maximum likelihood (ML) estimates of wire weights of RNN. The RNN-based GRN inference algorithms, ARD-RNN, [Formula: see text]-RNN, and ML-RNN are tested on simulated and experimental E. coli and yeast time series containing 6–11 genes and 7–19 data points. Published GRN inference algorithms based on regressions and mutual information networks are performed on the benchmark datasets to compare performances. Conclusion: ARD and [Formula: see text]-norm priors are used for the estimation of wire weights of RNN. Results of GRN inference experiments show that ARD-RNN, [Formula: see text]-RNN have similar best accuracies on the simulated time series. The ARD-RNN is more accurate than [Formula: see text]-RNN, ML-RNN, and mostly more accurate than the reference algorithms on the experimental time series. The effectiveness of ARD-RNN for inferring small-scale GRNs using gene expression time series of limited length is empirically verified.

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Forecasting High Frequency Data: An ARMA-Soft RBF Network Model for Time Series

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.160 ◽

2014 ◽

Vol 596 ◽

pp. 160-163

Author(s):

Dusan Marcek

Keyword(s):

Neural Networks ◽

Time Series ◽

Radial Basis Function ◽

Basis Function ◽

Time Series Data ◽

Forecast Accuracy ◽

Series Data ◽

Rbf Network ◽

Forecasting Models ◽

Radial Basis

In the article we alternatively develop forecasting models based on the Box-Jenkins methodology and on the neural approach based on classic and fuzzy logic radial basis function neural networks. We evaluate statistical and neuronal forecasting models for monthly platinum price time series data. In the direct comparison between statistical and neural models, the experiment shows that the neural approach clearly improve the forecast accuracy. Following fruitful applications of neural networks to predict financial data this work goes on. Both approaches are merged into one output to predict the final forecast values. The proposed novel approach deals with nonlinear estimate of various radial basis function neural networks.

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