Yoshio Noguchi

A clustering method of chromosome fluorescence profiles using modified self organizing map controlled by simulated annealing

The clustering method by the self organizing map algorithm of chromosome profiles measured by a slit-scan flow-cytometer is proposed. Moreover, the physical models of chromosomes have been introduced in order to take into account the rotation of chromosomes in the flow-cytometer. By this modification, the lengths of chromosomes and the intensity distribution of chromosome fluorescence can be estimated from chromosome profile data measured by the flow-cytometer. But the clustering results did not converge identically in some experiments and the distribution of the rotation angles was unnatural. So, we introduced simulated annealing to improve the convergence of our SOM algorithm. We compared the clustering results of this method with those of the K-means method and the SOM method.

The Analysis of Pen Inputs of Handwritten Symbols using Self Organizing Maps and its Application to User Authentication

To realize ubiquitous computing, mobile computers such as PDAs and smart phones will be important components. However, the mobile computers tend to be used without authentication because they should be usable as soon as the power becomes on. In this paper, we propose an authentication method without losing the usability of PDA. With this method, the user is authenticated using the pen pressure pattern measured by tracing the symbols which are displayed on the screen with stylus. From the analyses of pen pressure patterns using self organizing maps, the symbols which are suitable for authentication are selected. 70% of the users can be authenticated by this method from the authentication experiments using the symbols.

An application of genetic algorithm to DNA sequencing by oligonucleotide hybridization

The authors propose a sequencing algorithm for oligonucleotide hybridization using the genetic algorithm. The target DNA sequence reconstructed by the hybridization method is relatively long for genetic algorithm (GA), so special setups of the genetic operation are necessary. The authors introduce the grouping GA and a special crossover method for this problem. They carried out some experiments of sequence reconstruction, and examined the reconstructed sequences by comparing the motif length subsequences.

A design method of DNA chips for SNP analysis using self-organizing maps

We introduce a design method of DNA chips using self-organizing maps (SOM). DNA chips are powerful tools for sequencing and SNP (single nucleotide polymorphism) analyses of DNA sequences. A DNA chip is an array of DNA probes which are hybridized with the compliment sub-sequences in the target sequence. However, conventional DNA chips are showing tendency to be comprised of longer probes and get larger in size to achieve a higher resolution. To shrink the size of DNA chips, the design is considered to be important. To solve this problem, we applied SOM to obtain common features of DNA sequences with small number of probes which efficiently cover the target sequence with sufficient resolution for finding the correct position of SNPs. We evaluated the DNA chips designed by SOM with computer simulations of SNP analyses, changing the length of probes and size of the maps.

A design method of DNA chips using hierarchical self-organizing maps

We introduce a design method of DNA chips using self organizing maps (SOM) and hierarchical self-organizing maps (H-SOM). DNA chips are powerful tools for sequencings and SNP (single nucleotide polymorphism) analyses of DNA sequences. A DNA chip is an array of DNA probes which can be hybridized with complement subsequences in the target sequence. However, conventional DNA chips are showing tendency to be comprised of large number of long probes and get large in size to achieve high resolution. To shrink the size of DNA chips, design method is considered to be important. To solve this problem, we applied SOM to extract common features of DNA sequences using proper number of probes which efficiently cover the target sequence with sufficient resolutions. Furthermore, H-SOM can design the chip comprised of long probes more efficiently than SOM. We have designed some DNA chips from the sequence data in genome database using our SOM and H-SOM algorithm and evaluated the chips by computer simulations of SNP analyses.

Effective parameters derived from multiresolution analysis of surface electromyograms

Purpose of this study is to extract effective parameters for explaining muscle state based on multiresolution analysis of surface electromyograms (EMGs). The EMGs were decomposed into five levels using the Daubechies orthogonal wavelet of order 5 (db5). Two kinds of surface EMG were recorded from biceps brachii in five healthy males during muscle fatigue test and increasing load test. Selected parameters for analyzing EMGs were as follows; 1) PD(j): power of the details at level j ; 2) TPw: total power of the details concerning all levels; 3) RPD(j): power ratio of the PD(j) to TPw at level j. On muscle fatigue test, RPD(1), RPD(2), RPD(3) decreased after the test and RPD(4), RPD(5) increased contrarily. On increasing load test, RPD(3) showed a curve with two peaks at 25% MVC 30% MVC and at 45% MVC. On the contrary, RPD(4) showed inversed curve of the RPD(3) . This suggests activity of recruited fast muscle fibers.

A clustering method of chromosome fluorescence profiles using self organizing map

The clustering method by the self-organizing map algorithm of chromosome profiles measured by slit-scan flowcytometer is proposed. Chromosome profile represents the distribution of the fluorescence intensities along the lengthwise. To examine the performance of the cytometer developed in our laboratory, we made clustering experiments of the measured profiles. We developed a SOM based clustering algorithm using chromosome physical model, which can estimate the chromosome models and rotation angles, but the results were considered to be not complete. For this problem, we propose a new algorithm, which estimate the models and rotation angles step by step. We examine our algorithm using the virtual profiles and profiles measured by our cytometer.

A clustering method of chromosome fluorescence profiles by modified self organizing map

The clustering by the self-organizing map algorithm of chromosome profiles measured by slit-scan flowcytometer is proposed. Moreover, the physical models of chromosomes have been introduced in order to take into account the rotation of chromosomes in the flowcytometer. The self-organizing map algorithm has been improved so that it can modify the characteristic parameters of chromosome physical models. By this modification, the lengths of chromosomes and the intensity distribution of chromosome fluorescence can be estimated from chromosome profile data measured by the flowcytometer. The estimated lengths of chromosomes are almost equal to known values of the lengths of chromosomes. The clustering results by the above method are compared with the clustering results of the same data by the K-mean method and agglomerative hierarchical clustering.

Detection of Fast Fiber Recruitment by Multiresolution Analysis of Surface Electromyograms

This paper suggests possibility for detecting recruitment of fast muscle fiber on increasing load tests by multiresolution analysis of surface electromyograms (sEMGs). Recruitment of the muscle fiber can be guessed from a graph of power ratio vs. % maximum voluntary contraction concerning the detail at each level, RPD(j). RPD(j)s are extracted parameters from multiresolution analysis of sEMGs. The sEMGs were recorded from the biceps brachii in five healthy males

A Design Method of DNA Chips using Self Organizing Maps

In this paper, we introduce a design method of DNA chips using Self-Organizing Maps(SOM). DNA chips are powerful tools for sequencings and SNP(Single Nucleotide Polymorphism) analyses of DNA sequences. A DNA chip is an array of DNA probes which are hybridized with the compliment sub-sequences in the target sequence. However, conventional DNA chips are showing tendency to be comprised of longer probes and get larger in size to achieve a higher resolution. To shrink the size of DNA chips, the design is considered to be important. To solve this problem, we applied SOM to obtain common features of DNA sequences. Further, we improved the SOM algorithm to generate small number of probes which have different length and efficiently cover the target sequence with sufficient resolution for finding the correct position of SNPs. We evaluated the DNA chips designed by SOM with computer simulations of SNP analyses.