A Comparative Study of Clustering and Biclustering of Microarray Data

Saber, Haifa Ben; Elloumi, Mourad

doi:10.21859/jet-060223

Cited by 2 publications

(2 citation statements)

References 50 publications

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“…Figure 6 depicts an example of SOM network. We use the SOM algorithm for two main reasons: (1) it is a well-known approach for performing successfully data clustering and vector quantization [28,29]; (2) it has been proved that SOM algorithm converges when applied to one-dimensional features [30,31]. Each cluster defines areas of laptops' points determining a given range of magnetic field radiation for the laptops.…”

Section: Model Analysis By Self-organizing Map Clusteringmentioning

confidence: 99%

An approach to evaluation of the extremely low-frequency magnetic field radiation in the laptop computer neighborhood by artificial neural networks

Brodić

Tanikić

Amelio

2016

Neural Comput & Applic

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The paper considers the level of the extremely low-frequency magnetic field produced by laptop computers, which can be hazardous to the laptop users' health. It proposes a new model for prediction of the laptop magnetic field radiation starting from a priori known laptop characteristics using artificial neural network. In the experiment, the magnetic field of ten different laptop computers is measured. The measuring of the magnetic field is performed at the points, which are relevant to laptop computer users. Prediction is obtained from testing laptops. The predicted magnetic field results are evaluated by correlation coefficient and clustered for detection of magnetic field ranges by self-organizing map. Obtained cluster set is compared with the cluster set computed on the measured magnetic field at the same laptop characteristics. Correlation coefficient showed good correspondence of the predicted values with the measured values. Furthermore, the comparison between the two cluster sets revealed a good alignment of magnetic field ranges in measured and predicted results.

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Section: Model Analysis By Self-organizing Map Clusteringmentioning

confidence: 99%

An approach to evaluation of the extremely low-frequency magnetic field radiation in the laptop computer neighborhood by artificial neural networks

Brodić

Tanikić

Amelio

2016

Neural Comput & Applic

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“…Biclustering methods are proved to be NP-hard problem [ 13 ]. Therefore, according to the solution of the algorithm, the biclustering algorithms can be classified into various types [ 14 , 15 ]. Nevertheless, two major categories can be classified from the data, binary biclustering and non-binary biclustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis

Chu

Liu²,

Zhang³

et al. 2022

BMC Bioinformatics

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Biclustering algorithm is an effective tool for processing gene expression datasets. There are two kinds of data matrices, binary data and non-binary data, which are processed by biclustering method. A binary matrix is usually converted from pre-processed gene expression data, which can effectively reduce the interference from noise and abnormal data, and is then processed using a biclustering algorithm. However, biclustering algorithms of dealing with binary data have a poor balance between running time and performance. In this paper, we propose a new biclustering algorithm called the Adjacency Difference Matrix Binary Biclustering algorithm (AMBB) for dealing with binary data to address the drawback. The AMBB algorithm constructs the adjacency matrix based on the adjacency difference values, and the submatrix obtained by continuously updating the adjacency difference matrix is called a bicluster. The adjacency matrix allows for clustering of gene that undergo similar reactions under different conditions into clusters, which is important for subsequent genes analysis. Meanwhile, experiments on synthetic and real datasets visually demonstrate that the AMBB algorithm has high practicability.

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A Comparative Study of Clustering and Biclustering of Microarray Data

Cited by 2 publications

References 50 publications

An approach to evaluation of the extremely low-frequency magnetic field radiation in the laptop computer neighborhood by artificial neural networks

An approach to evaluation of the extremely low-frequency magnetic field radiation in the laptop computer neighborhood by artificial neural networks

A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis

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