Spherical and Torus SOM Approaches to Metabolic Syndrome Evaluation

Kihato, Peter K.; Tokutaka, Heizō; Ohkita, Masaaki; Fujimura, Kikuo; Kotani, Kazuhiko; Kurozawa, Yoichi; Maniwa, Yoshio

doi:10.1007/978-3-540-69162-4_29

Cited by 6 publications

(7 citation statements)

References 2 publications

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“…The promising approaches include the growing hierarchical SOM [21], the spherical and the torus SOM [22]. In the future, the applications of other approaches to the Pareto optimal solution visualization and their quantitative evaluations…”

Section: Resultsmentioning

confidence: 99%

Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps

Hironaka

Okamoto

Koakutsu

et al. 2015

SICE Journal of Control, Measurement, and System Integration

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: In the multi-objective optimization problem that appears naturally in the decision making process for the complex system, the visualization of the innumerable solutions called Pareto optimal solutions is important issue. This paper focuses on the Pareto optimal solution visualization method using the self-organizing maps which is one of promising visualization methods. The method has advantages in grasping the overall structure of the solutions and comparing the objective functions simultaneously. This method has been applied to some real problems, but its solution representation capability has not been studied well. This paper investigates the solution representation capability of the Pareto optimal solution visualization method using the SOM and points out its shortcomings. Then, two improvements are introduced to the visualization method. The effectiveness of the proposed method is confirmed via comparing it to the conventional visualization method using three indices evaluating the solution representation capability.

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Section: Resultsmentioning

confidence: 99%

Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps

Hironaka

Okamoto

Koakutsu

et al. 2015

SICE Journal of Control, Measurement, and System Integration

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“…We have employed a spherical self-organizing map (sSOM), which is also a nonhierarchical clustering method, to cluster genes by the gene expression profiles of cells and tissues. 4–9 In this study, we attempted to identify human glioma-specific cell surface markers by analyzing nine glioma-derived cell lines and compared normal adult and fetal brain tissues. Because the information relating to marker proteins on cells is extremely useful in identifying and targeting cells specifically, a sophisticated and conventional procedure to identify cell-specific surface markers is highly desirable.…”

Section: Introductionmentioning

confidence: 99%

A Unique Procedure to Identify Cell Surface Markers through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis

et al. 2016

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To identify cell-specific markers, we designed a DNA microarray platform with oligonucleotide probes for human membrane-anchored proteins. Human glioma cell lines were analyzed using microarray and compared with normal and fetal brain tissues. For the microarray analysis, we employed a spherical self-organizing map, which is a clustering method suitable for the conversion of multidimensional data into two-dimensional data and displays the relationship on a spherical surface. Based on the gene expression profile, the cell surface characteristics were successfully mirrored onto the spherical surface, thereby distinguishing normal brain tissue from the disease model based on the strength of gene expression. The clustered glioma-specific genes were further analyzed by polymerase chain reaction procedure and immunocytochemical staining of glioma cells. Our platform and the following procedure were successfully demonstrated to categorize the genes coding for cell surface proteins that are specific to glioma cells. Our assessment demonstrates that a spherical self-organizing map is a valuable tool for distinguishing cell surface markers and can be employed in marker discovery studies for the treatment of cancer.

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“…Based on these premises, we explored the visualization of Pareto-optimal solutions using SOMs (spherical SOMs [4] specifically) and evaluated the system using a GPUbased implementations. Spherical SOMs have been reported [5] to be less vulnerable to distortions compared to plane SOMs due to the fact that they have no edges.…”

Section: Introductionmentioning

confidence: 99%

“…Spherical SOMs have been reported [5] to be less vulnerable to distortions compared to plane SOMs due to the fact that they have no edges. They also show a comparatively rational spatial relationship among the data points and can be clustered more accurately [4,6].…”

Section: Introductionmentioning

confidence: 99%

Visualization of Pareto Solutions by Spherical Self-Organizing Map and It’s acceleration on a GPU

Yoshimi

Kuhara²,

Nishimoto³

et al. 2012

JSEA

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In this study, we visualize Pareto-optimum solutions derived from multiple-objective optimization using spherical self-organizing maps (SOMs) that lay out SOM data in three dimensions. There have been a wide range of studies involving plane SOMs where Pareto-optimal solutions are mapped to a plane. However, plane SOMs have an issue that similar data differing in a few specific variables are often placed at far ends of the map, compromising intuitiveness of the visualization. We show in this study that spherical SOMs allow us to find similarities in data otherwise undetectable with plane SOMs. We also implement and evaluate the performance using parallel sphere processing with several GPU environments.

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Spherical and Torus SOM Approaches to Metabolic Syndrome Evaluation

Cited by 6 publications

References 2 publications

Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps

Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps

A Unique Procedure to Identify Cell Surface Markers through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis

Visualization of Pareto Solutions by Spherical Self-Organizing Map and It’s acceleration on a GPU

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