**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**9

# Search results for: Yasuo Matsuyama

##### 9 Application of KL Divergence for Estimation of Each Metabolic Pathway Genes

**Authors:**
Shohei Maruyama,
Yasuo Matsuyama,
Sachiyo Aburatani

**Abstract:**

Development of a method to estimate gene functions is an important task in bioinformatics. One of the approaches for the annotation is the identification of the metabolic pathway that genes are involved in. Since gene expression data reflect various intracellular phenomena, those data are considered to be related with genes’ functions. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

**Keywords:**
Metabolic pathways,
gene expression data,
microarray,
Kullback–Leibler divergence,
KL divergence,
support
vector machines,
SVM,
machine learning.

##### 8 Research on the Correlation of the Fluctuating Density Gradient of the Compressible Flows

**Authors:**
Yasuo Obikane

**Abstract:**

**Keywords:**
Turbulence Modeling ,
Density Gradient Correlation,
Compressible

##### 7 A Frequency Dependence of the Phase Field Model in Laminar Boundary Layer with Periodic Perturbations

**Authors:**
Yasuo Obikane

**Abstract:**

**Keywords:**
Phase field model,
two phase flows,
Laminarboundary Layer

##### 6 Instability Problem of Turbo-Machines with Radial Distortion Problems

**Authors:**
Yasuo Obikane,
Sofiane Khelladi

**Abstract:**

**Keywords:**
inlet distorsion,
perturbation,
turbo-machine

##### 5 Effect of CW Laser Annealing on Silicon Surface for Application of Power Device

**Authors:**
Satoru Kaneko,
Takeshi Ito,
Kensuke Akiyama,
Manabu Yasui,
Chihiro Kato,
Satomi Tanaka,
Yasuo Hirabayashi,
Takeshi Ozawa,
Akira Matsuno,
Takashi Nire,
Hiroshi Funakubo,
Mamoru Yoshimoto

**Abstract:**

**Keywords:**
laser,
annealing,
silicon,
recrystallization,
thermal distribution,
resistivity,
finite element method,
absorption,
melting point,
latent heat of fusion.

##### 4 k-Neighborhood Template A-Type Three-Dimensional Bounded Cellular Acceptor

**Authors:**
Makoto Nagatomo,
Yasuo Uchida,
Makoto Sakamoto,
Tuo Zhang,
Tatsuma Kurogi,
Takao Ito,
Tsunehiro Yoshinaga,
Satoshi Ikeda,
Masahiro Yokomichi,
Hiroshi Furutani

**Abstract:**

**Keywords:**
Cellular acceptor,
configuration-reader,
converter,
finite automaton,
four-dimension,
on-line tessellation acceptor,
parallel/sequential array acceptor,
turing machine.

##### 3 A CT-based Monte Carlo Dose Calculations for Proton Therapy Using a New Interface Program

**Authors:**
A. Esmaili Torshabi,
A. Terakawa,
K. Ishii,
H. Yamazaki,
S. Matsuyama,
Y. Kikuchi,
M. Nakhostin,
H. Sabet,
A. Ishizaki,
W. Yamashita,
T. Togashi,
J. Arikawa,
H. Akiyama,
K. Koyata

**Abstract:**

**Keywords:**
Monte Carlo,
CT images,
Quadtree decomposition,
Interface program,
Proton beam

##### 2 Hierarchies Based On the Number of Cooperating Systems of Finite Automata on Four-Dimensional Input Tapes

**Authors:**
Makoto Sakamoto,
Yasuo Uchida,
Makoto Nagatomo,
Takao Ito,
Tsunehiro Yoshinaga,
Satoshi Ikeda,
Masahiro Yokomichi,
Hiroshi Furutani

**Abstract:**

In theoretical computer science, the Turing machine has played a number of important roles in understanding and exploiting basic concepts and mechanisms in computing and information processing [20]. It is a simple mathematical model of computers [9]. After that, M.Blum and C.Hewitt first proposed two-dimensional automata as a computational model of two-dimensional pattern processing, and investigated their pattern recognition abilities in 1967 [7]. Since then, a lot of researchers in this field have been investigating many properties about automata on a two- or three-dimensional tape. On the other hand, the question of whether processing fourdimensional digital patterns is much more difficult than two- or threedimensional ones is of great interest from the theoretical and practical standpoints. Thus, the study of four-dimensional automata as a computasional model of four-dimensional pattern processing has been meaningful [8]-[19],[21]. This paper introduces a cooperating system of four-dimensional finite automata as one model of four-dimensional automata. A cooperating system of four-dimensional finite automata consists of a finite number of four-dimensional finite automata and a four-dimensional input tape where these finite automata work independently (in parallel). Those finite automata whose input heads scan the same cell of the input tape can communicate with each other, that is, every finite automaton is allowed to know the internal states of other finite automata on the same cell it is scanning at the moment. In this paper, we mainly investigate some accepting powers of a cooperating system of eight- or seven-way four-dimensional finite automata. The seven-way four-dimensional finite automaton is an eight-way four-dimensional finite automaton whose input head can move east, west, south, north, up, down, or in the fu-ture, but not in the past on a four-dimensional input tape.

**Keywords:**
computational complexity,
cooperating system,
finite
automaton,
four-dimension,
hierarchy,
multihead.

##### 1 Oscillation Effect of the Multi-stage Learning for the Layered Neural Networks and Its Analysis

**Authors:**
Isao Taguchi,
Yasuo Sugai

**Abstract:**

This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.

**Keywords:**
data selection,
function approximation problem,
multistage leaning,
neural network,
voluntary oscillation.