A Universal Denoising and Peak Picking Algorithm for LC−MS Based on Matched Filtration in the Chromatographic Time Domain

Andreev, Victor P.; Rejtar, Tomáš; Chen, Hsuan-Shen; Moskovets, Eugene; Ivanov, and Alexander R.; Karger, Barry L.

doi:10.1021/ac0301806

Cited by 133 publications

(122 citation statements)

References 20 publications

Supporting

Mentioning

120

Contrasting

Order By: Relevance

“…Most of the literature on peak picking (e.g. Li et al, 1995;Andreev et al, 2003), in fact, deals with the problem of estimating the peaks (local maxima) of a process affected by noise. Smoothing methods are employed to eliminate the effect of the noise fluctuations, preserving the shape of the "clean" process.…”

Section: Methodsmentioning

confidence: 99%

Segment boundary detection via class entropy measurements in connectionist phoneme recognition

Salvi

2006

Speech Communication

View full text Add to dashboard Cite

This article investigates the possibility to use the class entropy of the output of a connectionist phoneme recogniser to predict time boundaries between phonetic classes. The rationale is that the value of the entropy should increase in proximity of a transition between two segments that are well modelled (known) by the recognition network since it is a measure of uncertainty. The advantage of this measure is its simplicity as the posterior probabilities of each class are available in connectionist phoneme recognition.The entropy and a number of measures based on differentiation of the entropy are used in isolation and in combination. The decision methods for predicting the boundaries range from simple thresholds to neural network based procedure.The different methods are compared with respect to their precision, measured in terms of the ratio between the number C of predicted boundaries within 10 or 20 msec of the reference and the total number of predicted boundaries, and recall, measured as the ratio between C and the total number of reference boundaries.

show abstract

Section: Methodsmentioning

confidence: 99%

Segment boundary detection via class entropy measurements in connectionist phoneme recognition

Salvi

2006

Speech Communication

View full text Add to dashboard Cite

show abstract

“…The signal should also be denoised to decrease the influence of interference and smooth the vibration curve. 21 In this study, the vibration signal was processed using the averaging method, which is based on the following formula…”

Section: Data Pre-processingmentioning

confidence: 99%

Monitoring of train-induced vibrations on rock slopes

Yan

Zhao

et al. 2017

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Train-induced vibrations will undoubtedly influence the stability of slopes near railway lines. To monitor the effects of such vibrations on slope stability, a wirelessly networked vibration test system was established, which included wirelessly networked vibration meters, high-precision and high-speed three-dimensional sensors and a remote wirelessly networked data server system. This system represents the first attempt to monitor the effects of train-induced vibrations on the stability of slopes in China. It enables real-time and long-distance monitoring by means of remote transmission with a low cost and high efficiency. The duration, frequency, amplitude, peak acceleration and peak particle velocity were adopted as measures for evaluating the influence of train vibrations. Simultaneously, we used additional monitoring technologies to verify the conclusions of the wirelessly networked vibration test system. The monitoring results indicated that the peak particle velocity was much higher near the track and gradually decreased with increasing distance. When the distance between the measurement point and the road axis was 12 m (H = 0 m), the maximal peak particle velocity was 0.4 mm/s, which remained below the maximum safe value.

show abstract

“…This fact has been used by a series of algorithms designed to purge chemical noise from this type of data. Andreev et al (2003) smoothed the time domain in LC-MS data using a matched filtering technique, which suppresses the additive noise in the Fourier domain taking into account its frequency characteristics. The component detection algorithm (CODA) by Windig, Phalp, and Payne (1996) calculated statistical descriptors to discard masses showing noisy elution profiles.…”

Section: Smoothing and Noise Estimationmentioning

confidence: 99%

Processing and classification of protein mass spectra

Hilario

Kalousis

Pellegrini

et al. 2006

Mass Spectrometry Reviews

158

141

View full text Add to dashboard Cite

Among the many applications of mass spectrometry, biomarker pattern discovery from protein mass spectra has aroused considerable interest in the past few years. While research efforts have raised hopes of early and less invasive diagnosis, they have also brought to light the many issues to be tackled before mass-spectra-based proteomic patterns become routine clinical tools. Known issues cover the entire pipeline leading from sample collection through mass spectrometry analytics to biomarker pattern extraction, validation, and interpretation. This study focuses on the data-analytical phase, which takes as input mass spectra of biological specimens and discovers patterns of peak masses and intensities that discriminate between different pathological states. We survey current work and investigate computational issues concerning the different stages of the knowledge discovery process: exploratory analysis, quality control, and diverse transforms of mass spectra, followed by further dimensionality reduction, classification, and model evaluation. We conclude after a brief discussion of the critical biomedical task of analyzing discovered discriminatory patterns to identify their component proteins as well as interpret and validate their biological implications. # 2006 Wiley Periodicals, Inc., Mass Spec Rev 25: 2006

show abstract

A Universal Denoising and Peak Picking Algorithm for LC−MS Based on Matched Filtration in the Chromatographic Time Domain

Cited by 133 publications

References 20 publications

Segment boundary detection via class entropy measurements in connectionist phoneme recognition

Segment boundary detection via class entropy measurements in connectionist phoneme recognition

Monitoring of train-induced vibrations on rock slopes

Processing and classification of protein mass spectra

Contact Info

Product

Resources

About