An Adaptive Segmentation Method Based on Gaussian Mixture Model (GMM) Clustering for DNA Microarray

Parthasarathy, Mohana Kuppuswamy; Ramya, R.; Vijaya, A.

doi:10.1109/icica.2014.24

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Traditional clustering methods include partition-based clustering, fuzzy clustering, hierarchical clustering, and density-based clustering. The most widely used algorithms are K-Means clustering, Gaussian mixture model (GMM) [14][15][16], and fuzzy cluster analysis. However, traditional clustering methods still have a long convergence time and low clustering accuracy when processing high-dimensional data.…”

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Chen

Mao

Wang

et al. 2020

Sensors

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Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam’s working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.

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Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Chen

Mao

Wang

et al. 2020

Sensors

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“…Moreover, adaptive pixel clustering for variable contours has been studied [ 10 – 14 ]. Spatial methods, such as the Snake Fisher model [ 15 , 16 ] or 3D spot modeling [ 17 ] have been introduced and Markov random field models have combined intensity and spatial information [ 18 , 19 ] for the spot segmentation. An efficient classification of pixels in background and foreground has been achieved by means of geometric measures [ 20 ] and by an algorithm based on growing con-centric hexagons [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised image segmentation for microarray spots with irregular contours and inner holes

et al. 2015

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BackgroundMicroarray analysis represents a powerful way to test scientific hypotheses on the functionality of cells. The measurements consider the whole genome, and the large number of generated data requires sophisticated analysis. To date, no gold-standard for the analysis of microarray images has been established. Due to the lack of a standard approach there is a strong need to identify new processing algorithms.MethodsWe propose a novel approach based on hyperbolic partial differential equations (PDEs) for unsupervised spot segmentation. Prior to segmentation, morphological operations were applied for the identification of co-localized groups of spots. A grid alignment was performed to determine the borderlines between rows and columns of spots. PDEs were applied to detect the inflection points within each column and row; vertical and horizontal luminance profiles were evolved respectively. The inflection points of the profiles determined borderlines that confined a spot within adapted rectangular areas. A subsequent k-means clustering determined the pixels of each individual spot and its local background.ResultsWe evaluated the approach for a data set of microarray images taken from the Stanford Microarray Database (SMD). The data set is based on two studies on global gene expression profiles of Arabidopsis Thaliana. We computed values for spot intensity, regression ratio, and coefficient of determination. For spots with irregular contours and inner holes, we found intensity values that were significantly different from those determined by the GenePix Pro microarray analysis software. We determined the set of differentially expressed genes from our intensities and identified more activated genes than were predicted by the GenePix software.ConclusionsOur method represents a worthwhile alternative and complement to standard approaches used in industry and academy. We highlight the importance of our spot segmentation approach, which identified supplementary important genes, to better explains the molecular mechanisms that are activated in a defense responses to virus and pathogen infection.

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An Automated and Efficient Approach for Spot Identification of Microarray Images Using X-Covariance

Karthik

Manjunath

2017

Proceedings of International Conference on Cognition and Recognition

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An Adaptive Segmentation Method Based on Gaussian Mixture Model (GMM) Clustering for DNA Microarray

Cited by 6 publications

References 5 publications

Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Unsupervised image segmentation for microarray spots with irregular contours and inner holes

An Automated and Efficient Approach for Spot Identification of Microarray Images Using X-Covariance

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