Merging microarray cell synchronization experiments through curve alignment

Hermans, Filip; Tsiporkova, Elena

doi:10.1093/bioinformatics/btl320

Cited by 34 publications

(23 citation statements)

References 17 publications

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“…DTW was initially introduced to recognize spoken words [25], but it has since been applied to a wide range of information retrieval and database problems. The description following is restricted to the most important steps of the algorithm (see [25], [35] for detailed explanation).…”

Section: B Elastic Similarity Measuresmentioning

confidence: 99%

Which DTW method applied to marine univariate time series imputation

Phan

Caillault

Lefebvre

et al. 2017

OCEANS 2017 - Aberdeen

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Abstract-Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imputation of marine univariate time series. In the first step, the missing data are completed on various mono-dimensional time series. To fill a missing sub-sequence (gap) in a time series, we first find the most similar sub-sequence to the sub-sequence before (resp. after) this gap according a Dynamic Time Warping (DTW)-cost. Then we complete the gap by the next (resp. previous) sub-sequence of the most similar one. Through experiments results on 5 different datasets we conclude that i) DTW gives the best results when considering the accuracy of imputation values and ii) Adaptive Feature Based DTW (AFBDTW) metric yields very similar shape of imputation values similar to the one of true values.

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Section: B Elastic Similarity Measuresmentioning

confidence: 99%

Which DTW method applied to marine univariate time series imputation

Phan

Caillault

Lefebvre

et al. 2017

OCEANS 2017 - Aberdeen

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show abstract

“…It was popularised in the '70s, when it was mainly applied to isolated word recognition and speech recognition [11,[15][16][17] to account for differences in speaking rates between speakers and utterances. Since then, it has been employed for clustering and classification in countless domains: electro-cardiogram analysis [18][19][20], clustering of gene expression profiles [21,22], biometrics [23,24], process monitoring [25]. Moreover, DTW has been also used in handwriting and online signature matching [10], sign language recognition and gesture recognition, data mining and time series clustering, computer vision and computer animation, surveillance, protein sequence alignment and chemical engineering, music and signal processing [26].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Zhen

Wang

et al. 2013

Mechanical Systems and Signal Processing

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Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency components with the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal, allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.

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“…Therefore, two penalty terms, which are not intuitively well based, were further introduced in [4]. A more straightforward approach was taken in [5], where the individual P-value for regulation and periodicity are combined through their geometric mean and thus the combined P-value can be seen as a sort of trade-off between the individual P-values, since it always ranges between their minimum and maximum. The geometric mean may as well produce a significantly low total P-value due to the occurrence of a very low individual P-value.…”

Section: Introductionmentioning

confidence: 99%

“…The geometric mean may as well produce a significantly low total P-value due to the occurrence of a very low individual P-value. Therefore, Hermans and Tsiporkova [5], introduced two separate significance conditions that both need to be verified in order to quantify a gene as a significantly periodic. The method of periodicity estimation employed in [5] is different from the one used by de Lichtenberg et al [4].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, Hermans and Tsiporkova [5], introduced two separate significance conditions that both need to be verified in order to quantify a gene as a significantly periodic. The method of periodicity estimation employed in [5] is different from the one used by de Lichtenberg et al [4]. Namely, it is based on a slightly adapted version of the method used by Merges et al [6,7], described originally by Shedden and Cooper [8].…”

Section: Introductionmentioning

confidence: 99%

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A hybrid computational method for the identification of cell cycle-regulated genes

Boeva

Ivanova

Lavesson

2010

2010 5th IEEE International Conference Intelligent Systems

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Abstract-Gene expression microarrays are the most commonly available source of high-throughput biological data. They have been widely employed in recent years for the definition of cell cycle regulated (or periodically expressed) subsets of the genome in a number of different organisms. These have driven the development of various computational methods for identifying periodical expressed genes. However, the agreement is remarkably poor when different computational methods are applied to the same data. In view of this, we are motivated to propose herein a hybrid computational method targeting the identification of periodically expressed genes, which is based on a hybrid aggregation of estimations, generated by different computational methods. The proposed hybrid method is benchmarked against three other computational methods for the identification of periodically expressed genes: statistical tests for regulation and periodicity and a combined test for regulation and periodicity. The hybrid method is shown, together with the combined test, to statistically significantly outperform the statistical test for periodicity. However, the hybrid method is also demonstrated to be significantly better than the combined test for regulation and periodicity.

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Merging microarray cell synchronization experiments through curve alignment

Abstract: Results, benchmark sets and scripts are freely available at our website: http://www.psb.ugent.be/cbd/publications.php

Cited by 34 publications

References 17 publications

Which DTW method applied to marine univariate time series imputation

Which DTW method applied to marine univariate time series imputation

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

A hybrid computational method for the identification of cell cycle-regulated genes

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