• Genomics & Informatics
• /
• v.6 no.4
• /
• pp.202-209
• /
• 2008

Biological pathways are known as collections of knowledge of certain biological processes. Although knowledge about a pathway is quite significant to further analysis, it covers only tiny portion of genes that exists. In this paper, we suggest a model to extend each individual pathway using a microarray expression data based on the known knowledge about the pathway. We take the Rosetta compendium dataset to extend pathways of Saccharomyces cerevisiae obtained from KEGG (Kyoto Encyclopedia of genes and genomes) database. Before applying our model, we verify the underlying assumption that microarray data reflect the interactive knowledge from pathway, and we evaluate our scoring system by introducing performance function. In the last step, we validate proposed candidates with the help of another type of biological information. We introduced a pathway extending model using its intrinsic structure and microarray expression data. The model provides the suitable candidate genes for each single biological pathway to extend it.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.2
• /
• pp.194-199
• /
• 2003

Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1338-1343
• /
• 2003

Data is the expression of the language or numerical values that show some characteristics. And information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns and make decisions. Today, knowledge extraction and application of the knowledge are broadly accomplished to improve the comprehension and to elevate the performance of systems in several industrial fields. The knowledge extraction could be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge can be drawn by rules. Clustering (CU, input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for expression the knowledge by rules. In this paper, the various approaches of the knowledge extraction are examined by categories that separate the methods by the applied industrial fields. Also, the several test data and the experimental results are compared and analysed based upon the applied techniques that include CL, ISP, NF, NN, EM, and so on.

• Proceedings of the Korea Environmental Mutagen Society Conference
• /
• 2002.11a
• /
• pp.14-40
• /
• 2002

15-Deoxy-${\Delta}^{12, 14}$-prostaglandin $J_2$ (15-deoxy-$PGJ_2$), a naturally occurring ligand activates the peroxisome proliferator-activated $receptor-{\gamma}(PPAR-{\gamma}$). Activation of $PPAR-{\gamma}$ has been found to induce cell differentiation such as adipose cell and macrophage. Here it was investigated whether 15-deoxy-$PGJ_2$ has neuronal cell differentiation and possible underlying molecular mechanisms. Dopaminergic differentiating PC 12 cells treated with 15-deoxy-$PGJ_2$ (0.2 to 1.6 ${\mu}M$) alone showed measurable neurite extension and expression of neurofilament, markers of cell differentiation. However much greater extent of neurite extension and expression of neurofilament was observed in the presence of NGF (50 ng/ml). In parallel with its increasing effect on the neurite extension and expression of neurofilament, 15-deoxy-$PGJ_2$ enhanced NGF-induced p38 MAP kinase expression and its phosphorylation in addition to the activation of transcription factor AP-1 in a dose dependent manner. Moreover, pretreatment of SD 203580, a specific inhibitor of p38 MAP kinase inhibited the promoting effect of 15-deoxy-$PGJ_2$(0.8 ${\mu}M$) on NGF-induced neurite extension. This inhibition correlated well with the ability of SB203580 to inhibit the enhancing effect of 15-deoxy-$PGJ_2$ on the expression of p38 MAP kinase and activation of AP-1, The promoting ability of 15-deoxy-$PGJ_2$ did not occur through $PPAR-{\gamma}$, as synthetic PPAR-${\gamma}$ agonist andantagonist did not change the neurite promoting effect of 15-deoxy-PGJ$_2$. In addition, contrast to other cells (embryonic midbrain and SK-N-MC cells), $PPAR-{\gamma}$ was not expressed in PC-12 cells. Other structure related prostaglandins, PGD$_2$ and $PGE_2$ acting via a cell surface G-protein-coupled receptor (GPCR) did not increase basal or NGF-induced neurite extension. Moreover, GPCR (EP and DP receptor) antagonists did not alter the promoting effect of f 5-deoxy-$PGJ_2$ on neurite extension and activation of p38 MAP kinase, suggesting that the promoting effect of 15-deoxy-$PGJ_2$ may not be mediated GPCR. These data demonstrate that activation of p38 MAP kinase in conjunction with AP-1 single pathway may be important in the promoting activity of 15-deoxy-$PGJ_2$ cells.

• Proceedings of the Korean Society of Toxicology Conference
• /
• 2002.11b
• /
• pp.14-40
• /
• 2002

15-Deoxy- Δ$\^$12,14/-prostaglandin J$_2$ (15-deoxy-PGJ$_2$), a naturally occurring ligand activates the peroxisome proliferator-activated receptor-${\gamma}$ (PPAR-${\gamma}$). Activation of PPAR-y has been found to induce cell differentiation such as adipose cell and macrophage. Here it was investigated whether 15-deoxy-PGJ$_2$ has neuronal cell differentiation and possible underlying molecular mechanisms. Dopaminergic differentiating PC 12 cells treated with 15-deoxy-PGJ$_2$ (0.2 to 1.6 ${\mu}$M) alone showed measurable neurite extension and expression of neurofilament, markers of cell differentiation. However much greater extent of neurite extension and expression of neurofilament was observed in the presence of NGF (50 ng/$m\ell$). In parallel with its increasing effect on the neurite extension and expression of neurofilament, 15-deoxy-PGJ$_2$ enhanced NGF-induced p38 MAP kinase expression and its phosphorylation in addition to the activation of transcription factor AP-1 in a dose dependent manner. Moreover, pretreatment of SD 203580, a specific inhibitor of p38 MAP kinase inhibited the promoting effect of 15-deoxy-PGJ$_2$ (0.8 ${\mu}$M) on NGF-induced neurite extension. This inhibition correlated well with the ability of SB203580 to inhibit the enhancing effect of 15-deoxy-PGJ$_2$ on the expression of p38 MAP kinase and activation of AP-1. The promoting ability of 15-deoxy-PGJ$_2$ did not occur through PPAR-${\gamma}$, as synthetic PPAR-${\gamma}$ agonist and antagonist did not change the neurite promoting effect of 15-deoxy-PGJ$_2$. In addition, contrast to other cells (embryonic midbrain and SK-N-MC cells), PPAR-${\gamma}$ was not expressed in PC-12 cells. Other structure related prostaglandins, PGD$_2$ and PGE$_2$ acting via a cell surface G-protein-coupled receptor (GPCR) did not increase basal or NGF-induced neurite extension. Moreover, GPCR (EP and DP receptor) antagonists did not alter the promoting effect of 15-deoxy-PGJ$_2$ on neurite extension and activation of p38 MAP kinase, suggesting that the promoting effect of 15-deoxy-PGJ$_2$ may not be mediated GPCR. These data demonstrate that activation of p38 MAP kinase in conjunction with AP-1 signal pathway may be important in the promoting activity of 15-deoxy-PGJ$_2$ on the differentiation of PC12 cells.

• Journal of Information Science Theory and Practice
• /
• v.7 no.2
• /
• pp.32-39
• /
• 2019

A convolutional neural network (CNN) has been widely used in facial expression recognition (FER) because it can automatically learn discriminative appearance features from an expression image. To make full use of its discriminating capability, this paper suggests a simple but effective method for CNN based FER. Specifically, instead of an original expression image that contains facial appearance only, the expression image with facial geometry visualization is used as input to CNN. In this way, geometric and appearance features could be simultaneously learned, making CNN more discriminative for FER. A simple CNN extension is also presented in this paper, aiming to utilize geometric expression change derived from an expression image sequence. Experimental results on two public datasets (CK+ and MMI) show that CNN using facial geometry visualization clearly outperforms the conventional CNN using facial appearance only.

• Korean Journal of Computational Design and Engineering
• /
• v.11 no.6
• /
• pp.393-402
• /
• 2006

The purpose of this study is to develop mechanisms of nD model-based design by the combination of 2D drawing standards and 3D building models from the current 2D and text-based design. The aim of this study can be archived by defining the 2D model extension definitions for the IFC model development and harmonizing existing 2D standards. The paper examines 1) 3D Representation of Building Element and Building Services element, and 2D Model extension of IFC2X.2, 2) Basic development of additional 2D element that should be added to IFC model, and 3) mapping method between current 2D standard and IFC2.X2. Following this approach, the interoperability problem between 3D model and 2D drawing can be solved and finally an extended data model could be developed.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.2
• /
• pp.111-119
• /
• 2023

In this paper, we investigate the feasibility of functional programming in the Scala language. The main issue is to what extent Scala is able to handle major properties of functional programming such as lambda expression, high-order functions, generic types, algebraic data types, and monads. For this purpose, we implement an interpreter of an imperative language. In this implementation, the same functional programming techniques are applied to both Haskell and Scala languages, and then these two versions of implementations are compared and analyzed. The abstract syntax tree of an imperative language is expressed as algebraic data types with generics and enum classes in Scala, and the state transition of imperative languages is implemented by using state monad. Extension and given, new features of Scala, are used as well.

• Journal of the Korea Convergence Society
• /
• v.12 no.2
• /
• pp.7-13
• /
• 2021

This paper introduces an algorithm that compensates for missing values after converting them into a format that can represent the probability for incomplete data including missing values in training data. In the previous method using this data conversion, incomplete data was processed by allocating missing values with an equal probability that missing variables can have. This method applied to many problems and obtained good results, but it was pointed out that there is a loss of information in that all information remaining in the missing variable is ignored and a new value is assigned. On the other hand, in the new proposed method, only complete information not including missing values is input into the well-known classification algorithm (C4.5), and the decision tree is constructed during learning. Then, the probability of the missing value is obtained from this decision tree and assigned as an estimated value of the missing variable. That is, some lost information is recovered using a lot of information that has not been lost from incomplete learning data.

• Journal of Endocrine Surgery
• /
• v.18 no.4
• /
• pp.228-235
• /
• 2018

Purpose: The epidermal growth factor receptor (EGFR) family plays a crucial role in the growth of malignant tumors. EGFR and human EGFR 2 (HER2) protein overexpression are associated with an unfavorable prognosis and are important therapeutic targets in breast cancer. The aim of this study was to evaluate the relationship between EGFR and HER2 expression and clinicopathological factors in papillary thyroid carcinoma (PTC) at a single institution. Methods: A total of 129 consecutive patients with PTC were enrolled in this study and underwent thyroid surgery between October 2013 and February 2015. EGFR and HER2 protein expression was evaluated in the 129 primary tumors by immunohistochemistry, and the results were compared with the clinicopathological features. Results: Of the 129 PTC tumors, 20 (15.5%) were HER2 positive, and 109 (84.5%) were HER2 negative. Moreover, EGFR positivity were observed in 111 (86%) tumors. The mean age of the patients was $46.3{\pm}11.9years$ (range, 20-74 years), and the mean tumor size was $1.08{\pm}0.75cm$ (range, 0.2-3.5 cm). Tumor size, extrathyroidal extension, histological subtype, and TNM stage were not significantly associated with EGFR or HER2 expression. Meanwhile, high Ki-67 labeling index was significantly associated with EGFR expression (P=0.002), HER2 expression was significantly associated with younger age (${\leq}45years$) and cervical lymph node metastasis. Conclusion: Based on our data, it is not clear whether EGFR and HER2 expression is associated with tumor aggressiveness in PTC.