Aligning gene expression time series with time warping algorithms

By John D Aach, George M. Church. Bioinform., 2001

546 citations recorded by the source corpus.

Subfields: Food Science, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/23164d56e286cc62397fde4232c6c0b50071e6e0

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Citations in the corpus

Under Consideration for Publication in Knowledge and Information Systems Improving Svm Classification on Imbalanced Time Series Data Sets with Ghost Points (unknown): “The DTW distance has been shown to be superior to the Euclidean distance in many cases [1,36].”
A global averaging method for dynamic time warping, with applications to clustering (2011, methodology): “: +33 3 68 85 ail addresses: fpetitjean@unistra.fr (F. Petitjea nistra.fr (A. Ketterlin), gancarski@unistra.fr (P a b s t r a c t Mining sequential data is an old topic that has been revived in the last decade, due to the increasing availability of sequential datasets.”
Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package (2009, background): “…domains: electro-cardiogram analysis (Huang and Kinsner 2002; Syeda-Mahmood et al. 2007; Tuzcu and Nas 2005), clustering of gene expression proﬁles (Aach and Church 2001; Hermans and Tsiporkova 2007), biometrics (Faundez-Zanuy 2007; Rath and Manmatha 2003), process monitoring (Gollmer and Posten…”
Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data (2004, background): “In recent years it has found application in chromatography [2–4], in batch process monitoring [16–20] and in gene expression studies [21,22].”
Functional Modelling and Classification of Longitudinal Data * (2005, background): “…scalar or multivariate observations are complemented by observations in the form of random trajectories, such as time-course gene expression data (Aach & Church, 2001; Liu & Müller, 2003; Zhao et al., 2004), physiological time courses (Ratcliffe et al., 2002) or industrial and quality control…”
Analyzing time series gene expression data (2004, methodology): “Aach and Church (2001) use linear interpolation to estimate gene expression levels for unobserved time points.”
Exact indexing of dynamic time warping (2002, methodology): “Once again, we feel that this is not really a restriction because virtually every practitioner we are aware of reiterates the absolute necessity of using constraints (Aach and Church 2001; Berndt and Clifford 1994; Caiani et al. 1998; Chu et al. 2002; Gollmer and Posten 1995; Itakura 1975; Rabiner e”
Indexing multi-dimensional time-series with support for multiple distance measures (2003, methodology): “Because of this, DTW is used for gene expression data mining [1, 3].”
Making Time-Series Classification More Accurate Using Learned Constraints (2004, methodology): “The measurement of similarity between two time series is an important subroutine in many data mining applications, including rule discovery [12], clustering [1] [14], anomaly detection [13], motif discovery [8], and classification [15][22].”
Human activities recognition using depth images (2013, background): “The formulation of the sequence alignment in the above share similarities to the classical time warping, but has an important difference in that time warping considers the distances of vector pairs taken from a common k-dimensional feature space, whereas sequence alignment considers the distances of”
Time-series clustering - A decade review (2015, unknown): “Better accuracy than Euclidean distance [129,114,120,90].”
Three Myths about Dynamic Time Warping Data Mining (2005, background): “Unauthorized reproduction of this article is prohibited 507 global constraint [ 1 ][14][18].”
Functional Modelling and Classification of Longitudinal Data. (2005, background): “Application areas of FDA are likely to encompass many fields of statistics where scalar or multivariate observations are complemented by observations in the form of random trajec - tories , such as time - course gene expression data ( Aach & Church , 2001 ; Liu & Muïler , 2003 ; Zhao et al .”
Path Similarity Skeleton Graph Matching (2008, methodology): “The Dynamic Time Warping (DTW) distance has been shown to be superior to the Euclidean distance of sequences in many cases [ 40 ][41][42][43].”
Generalizing DTW to the multi-dimensional case requires an adaptive approach (2016, unknown): “This constraint prevents pathological warpings (for example, a single heartbeat mapping to ten heartbeats) and is at the heart of the LB Keogh lowerbounding technique, which is used in virtually all speedup techniques for DTW (Aach and Church 2001; Ding et al. 2008).”