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Multiresolution analysis of point processes and statistical thresholding for Haar wavelet-based intensity estimation. ANN I STAT MATH 2020. [DOI: 10.1007/s10463-020-00753-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Shekar S, Chien CC, Hartel A, Ong P, Clarke OB, Marks A, Drndic M, Shepard KL. Wavelet Denoising of High-Bandwidth Nanopore and Ion-Channel Signals. NANO LETTERS 2019; 19:1090-1097. [PMID: 30601669 PMCID: PMC6904930 DOI: 10.1021/acs.nanolett.8b04388] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent work has pushed the noise-limited bandwidths of solid-state nanopore conductance recordings to more than 5 MHz and of ion channel conductance recordings to more than 500 kHz through the use of integrated complementary metal-oxide-semiconductor (CMOS) integrated circuits. Despite the spectral spread of the pulse-like signals that characterize these recordings when a sinusoidal basis is employed, Bessel filters are commonly used to denoise these signals to acceptable signal-to-noise ratios (SNRs) at the cost of losing many of the faster temporal features. Here, we report improvements to the SNR that can be achieved using wavelet denoising instead of Bessel filtering. When combined with state-of-the-art high-bandwidth CMOS recording instrumentation, we can reduce baseline noise levels by over a factor of 4 compared to a 2.5 MHz Bessel filter while retaining transient properties in the signal comparable to this filter bandwidth. Similarly, for ion-channel recordings, we achieve a temporal response better than a 100 kHz Bessel filter with a noise level comparable to that achievable with a 25 kHz Bessel filter. Improvements in SNR can be used to achieve robust statistical analyses of these recordings, which may provide important insights into nanopore translocation dynamics and mechanisms of ion-channel function.
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Affiliation(s)
| | - Chen-Chi Chien
- Department of Physics and Astronomy , University of Pennsylvania , Philadelphia , Pennsylvania 19104 , United States
| | | | | | - Oliver B Clarke
- Department of Physiology and Cellular Biophysics , Columbia University , New York , New York 10032 , United States
| | - Andrew Marks
- Department of Physiology and Cellular Biophysics , Columbia University , New York , New York 10032 , United States
| | - Marija Drndic
- Department of Physics and Astronomy , University of Pennsylvania , Philadelphia , Pennsylvania 19104 , United States
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Chaubey YP, Shirazi E. On MISE of a Non linear Wavelet Estimator of the Regression Function Based on Biased Data under Strong Mixing. COMMUN STAT-THEOR M 2015. [DOI: 10.1080/03610926.2014.990285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Chaubey YP, Chesneau C, Shirazi E. Wavelet-based estimation of regression function for dependent biased data under a given random design. J Nonparametr Stat 2013. [DOI: 10.1080/10485252.2012.734619] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Hotz T, Marnitz P, Stichtenoth R, Davies L, Kabluchko Z, Munk A. Locally adaptive image denoising by a statistical multiresolution criterion. Comput Stat Data Anal 2012. [DOI: 10.1016/j.csda.2011.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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6
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Guo B, Wu Y, Xie H, Miao B. A segmented regime-switching model with its application to stock market indices. J Appl Stat 2011. [DOI: 10.1080/02664763.2010.545374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Urbas AA, Choquette SJ. Automated spectral smoothing with spatially adaptive penalized least squares. APPLIED SPECTROSCOPY 2011; 65:665-677. [PMID: 21639989 DOI: 10.1366/10-05971] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
A variety of data smoothing techniques exist to address the issue of noise in spectroscopic data. The vast majority, however, require parameter specification by a knowledgeable user, which is typically accomplished by trial and error. In most situations, optimized parameters represent a compromise between noise reduction and signal preservation. In this work, we demonstrate a nonparametric regression approach to spectral smoothing using a spatially adaptive penalized least squares (SAPLS) approach. An iterative optimization procedure is employed that permits gradual flexibility in the smooth fit when statistically significant trends based on multiscale statistics assuming white Gaussian noise are detected. With an estimate of the noise level in the spectrum the procedure is fully automatic with a specified confidence level for the statistics. Potential application to the heteroscedastic noise case is also demonstrated. Performance was assessed in simulations conducted on several synthetic spectra using traditional error measures as well as comparisons of local extrema in the resulting smoothed signals to those in the true spectra. For the simulated spectra, a best case comparison with the Savitzky-Golay smoothing via an exhaustive parameter search was performed while the SAPLS method was assessed for automated application. The application to several dissimilar experimentally obtained Raman spectra is also presented.
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Affiliation(s)
- Aaron A Urbas
- Biochemical Science Division, Chemical Science and Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8395, USA. aaron.urbas@ nist.gov
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Ogden RT, Greene E. Wavelet modeling of functional random effects with application to human vision data. J Stat Plan Inference 2010. [DOI: 10.1016/j.jspi.2010.04.044] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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NEUMEYER NATALIE, KEILEGOM INGRIDVAN. Change-Point Tests for the Error Distribution in Non-parametric Regression. Scand Stat Theory Appl 2009. [DOI: 10.1111/j.1467-9469.2009.00639.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Li Y, Wen Z, Zhou C, Tan F, Li M. Effects of neighboring sequence environment in predicting cleavage sites of signal peptides. Peptides 2008; 29:1498-504. [PMID: 18635288 DOI: 10.1016/j.peptides.2008.04.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2008] [Revised: 04/08/2008] [Accepted: 04/17/2008] [Indexed: 10/22/2022]
Abstract
Signal peptide has a pivotal role in the translocation of secretory protein. Some models have been designed to predict its cleavage site. It is reported that the cleavage site has relationship with the neighboring sequence environment, i.e., hydrophobic core h-region, and the specific patterns in c-region. In some studies, this finding does facilitate the prediction of cleavage site. However, in these models, sequence environment information is merely taken account of as model inputs and no detailed investigation into its effect on the prediction of cleavage site has been made. In this work, we analyze the constraint on cleave site placed by the hydrophobic core of signal peptide and then use it to improve the performance of the signal peptide cleavage site prediction. Our model is designed as follows: firstly, a sliding window is used to scan sample and artificial neural network (ANN) is employed to give cleavage site/non-cleavage site scores. Then, based on an estimated hydrophobic h-region a correcting function is proposed to improve the prediction result, in which the sequence environment is taken into account. A trend of cleavage site is indicated by our analysis for each position, which is consistent with experimental findings. Through this correcting step, the improvement of prediction accuracy is over 7%. It therefore demonstrates the neighboring sequence environment is helpful for determination of cleavage site. Program written in Matlab can be downloaded from http://www.scucic.cn/combined model/source code.html.
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Affiliation(s)
- Yizhou Li
- College of Chemistry, Sichuan University, 610064 Chengdu, PR China
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Antoniadis A, Leporini D, Pesquet J. Wavelet thresholding for some classes of non–Gaussian noise. STAT NEERL 2008. [DOI: 10.1111/1467-9574.00211] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- A. Antoniadis
- Université Joseph Fourier, France and Université de Marne–la–Vallée, Cité, Descartes
| | - D. Leporini
- Université Joseph Fourier, France and Université de Marne–la–Vallée, Cité, Descartes
| | - J.–C. Pesquet
- Université Joseph Fourier, France and Université de Marne–la–Vallée, Cité, Descartes
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Lavrik I, Young Jung Y, Ruggeri F, Vidakovic B. Bayesian False Discovery Rate Wavelet Shrinkage: Theory and Applications. COMMUN STAT-SIMUL C 2008. [DOI: 10.1080/03610910802049649] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Boubchir L, Fadili JM. A closed-form nonparametric Bayesian estimator in the wavelet domain of images using an approximate α-stable prior. Pattern Recognit Lett 2006. [DOI: 10.1016/j.patrec.2006.01.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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GANESAN RAJESH, DAS TAPASK, VENKATARAMAN VIVEKANAND. Wavelet-based multiscale statistical process monitoring: A literature review. ACTA ACUST UNITED AC 2004. [DOI: 10.1080/07408170490473060] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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16
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Lee G. Choice of smoothing parameters in wavelet series estimators. J Nonparametr Stat 2003. [DOI: 10.1080/1048525031000120251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Abstract
Recently there has been significant development in the use of wavelet methods in various data mining processes. However, there has been written no comprehensive survey available on the topic. The goal of this is paper to fill the void. First, the paper presents a high-level data-mining framework that reduces the overall process into smaller components. Then applications of wavelets for each component are reviewd. The paper concludes by discussing the impact of wavelets on data mining research and outlining potential future research directions and applications.
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Affiliation(s)
- Tao Li
- Univ. of Rochester, Rochester, NY
| | - Qi Li
- Univ. of Delaware, Newark, DE
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Huang HC, Cressie N. Deterministic/Stochastic Wavelet Decomposition for Recovery of Signal From Noisy Data. Technometrics 2000. [DOI: 10.1080/00401706.2000.10486047] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Cai C, Harrington PDB. Different Discrete Wavelet Transforms Applied to Denoising Analytical Data. ACTA ACUST UNITED AC 1998. [DOI: 10.1021/ci980210j] [Citation(s) in RCA: 50] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Chunsheng Cai
- Department of Chemistry and Biochemistry, Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Ohio University, Athens, Ohio 45701-2979
| | - Peter de B. Harrington
- Department of Chemistry and Biochemistry, Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Ohio University, Athens, Ohio 45701-2979
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