• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1069-1077
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• 2014

A clubfoot is a kind of congenital deformity of foot, which is internally rotated at the ankle. In this paper, we are going to cluster the curves of relative differences between regular and operated feet. Since these curves are irregular and sparsely sampled, general clustering models could not be applied. So the clustering model for sparsely sampled functional data by James and Sugar (2003) are applied and parameters are estimated using EM algorithm. The number of clusters is determined by the distortion function (Sugar and James, 2003) and two clusters of the curves are found.

• International Journal of Contents
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• v.8 no.4
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• pp.56-63
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• 2012

In recent decades, analyzing the activities of human brain achieved some accomplishments by using the functional Magnetic Resonance Imaging (fMRI) technique. fMRI data provide a sequence of three-dimensional images related to human brain's activity which can be used to detect instantaneous cognitive states by applying machine learning methods. In this paper, we propose a new approach for distinguishing human's cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative sentence". Since fMRI data are high dimensional (about 100,000 features in each sample), extremely sparse and noisy, feature selection is a very important step for increasing classification accuracy and reducing processing time. We used the Fisher Discriminant Ratio to select the most powerful discriminative features from some Regions of Interest (ROIs). The experimental results showed that our approach achieved the best performance compared to other feature extraction methods with the average accuracy approximately 95.83% for the first study and 99.5% for the second study.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.1-8
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• 2021

To simulate a process model in the field of chemical engineering, it is very important to identify the physical properties of novel materials as well as existing materials. However, it is difficult to measure the physical properties throughout a set of experiments due to the potential risk and cost. To address this, this study aims to develop a property prediction model based on the group contribution method for aromatic chemical compounds including benzene rings. The benzene rings of aromatic materials have a significant impact on their physical properties. To establish the prediction model, 42 important functional groups that determine the physical properties are considered, and the total numbers of functional groups on 147 aromatic chemical compounds are counted to prepare a dataset. Support vector regression is employed to prepare a prediction model to handle sparse and high-dimensional data. To verify the efficacy of this study, the results of this study are compared with those of previous studies. Despite the different datasets in the previous studies, the comparison indicated the enhanced performance in this study. Moreover, there are few reports on predicting the physical properties of aromatic compounds. This study can provide an effective method to estimate the physical properties of unknown chemical compounds and contribute toward reducing the experimental efforts for measuring physical properties.

• Applied Microscopy
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• v.14 no.2
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• pp.29-37
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• 1984

Morphological changes of the epithelium of the endometrium by prolonged treatment of $17{\beta}$-estradiol or progesterone in ovariectomized rats was studied at the ultrastructural level. The epithelium of the endometrium in ovariectomized rats was characterized by the appearance of a number of vacuoles which was contained with the membraneous structures, lipid droplets and the others. The epithelium was low cuboidal, and a few short microvilli were present at the cell surface. Secretory granules are rarely found. After estradiol treatment, the epithelium was high columnar in shape. The mitochondria was appeared throughout the cytoplasm, however, long or swelling mitochondria was often found. Golgi apparatus and rER were relatively well-developed. Relatively long and sparse microvilli were present at the cell surface. After progesterone treatment, the epithelium was characterized by the appearance of numerous vesicles at the apical region and numerous lipid droplets at the subnuclear region. At the cell surface a number of short and blunt microvilli were found. These data indicated that the endometrium was dependent on estrogen and progesterone for changes in both its morphological and functional state and suggested that each hormone exerted a unique effect on the epithelial cells.