Ju Han Kim

Bio: Ju Han Kim is an academic researcher from New Generation University College. The author has contributed to research in topics: Canopy clustering algorithm & Fuzzy clustering. The author has an hindex of 5, co-authored 8 publications receiving 136 citations.

The JNK-dependent CaMK pathway restrains the reversion of committed cells during osteoclast differentiation

Abstract: Osteoclastogenesis involves the commitment of macrophage-lineage precursors to tartrate-resistant acid phosphatase-positive (TRAP+) mononuclear pre-osteoclasts (pOCs) and subsequent fusion of pOCs to form multinuclear mature osteoclasts. Despite many studies on osteoclast differentiation, little is known about the signaling mechanisms that specifically mediate the osteoclastic commitment. In this study, we found that inhibition of JNK at the pOC stage provoked reversion of TRAP(+) cells to TRAP(-) cells. The conversion to TRAP(-) cells occurred with concomitant return to the state with higher expression of macrophage antigens, and greater activity of phagocytosis and dendritic-differentiation potential. JNK inhibition at the pOC stage reduced NFATc1 and CaMK levels, and addition of active NFATc1 partially rescued the effect of JNK inhibition. In addition, the level of NFATc1 was decreased by knockdown of CaMK by RNAi and by catalytic inhibition of CaMK, which both caused the reversion of pOCs to macrophages. These data suggest that JNK activity is specifically required for maintaining the committed status during osteoclastogenesis and that the CaMK-NFATc1 pathway is the key element in that specific role of JNK.

Heterogeneous clustering ensemble method for combining different cluster results

Abstract: Biological data set sizes have been growing rapidly with the technological advances that have occurred in bioinformatics. Data mining techniques have been used extensively as approaches to detect interesting patterns in large databases. In bioinformatics, clustering algorithm technique for data mining can be applied to find underlying genetic and biological interactions, without considering prior information from datasets. However, many clustering algorithms are practically available, and different clustering algorithms may generate dissimilar clustering results due to bio-data characteristics and experimental assumptions. In this paper, we propose a novel heterogeneous clustering ensemble scheme that uses a genetic algorithm to generate high quality and robust clustering results with characteristics of bio-data. The proposed method combines results of various clustering algorithms and crossover operation of genetic algorithm, and is founded on the concept of using the evolutionary processes to select the most commonly-inherited characteristics. Our framework proved to be available on real data set and the optimal clustering results generated by means of our proposed method are detailed in this paper. Experimental results demonstrate that the proposed method yields better clustering results than applying a single best clustering algorithm.

Gene Expression Profiling of Human Gastrointestinal Stromal Tumors According to Its Malignant Potential

Abstract: Surgical removal or treatment with Imatinib mesylate (STI-571/Gleevec) is shown to be highly effective in gastrointestinal stromal tumors (GISTs). However, it is unclear the understanding of the molecular basis in GISTs according to its malignant potential. The aim of this study was therefore to determine the gene expression profiles according to GISTs risk progresses. In this study, we performed a cDNA microarray with 30 human GIST tissues using the Mac Array-Express 10K chip (10,800 genes), and compared their gene expression profiles among low (n = 10), intermediate (n = 8), and high-risk groups (n = 12) according to NIH consensus criteria. A total of 181 genes were identified to be expressed differentially according to GISTs risk category. After clustering by self-organizing maps, the expression profiles of 32 genes sequentially increased as the tumor risk increased, and those of 37 genes sequentially decreased as the tumor risk increased. Identified targets have been cross referenced against their involvements in different cellular pathways, according to GenMAPP, KEGG, and BioCarta. In pathway-enrichment analysis, eight up-regulated pathways and ten down-regulated pathways were significantly enriched. Our results showed a remarkably distinct and uniform expression pattern in GISTs progression. Moreover, the expression profiling of GISTs may be used as a basic reference to better understand the molecular basis of GISTs tumorigenesis and to identify a novel target molecule for replacing KIT and PDGFRA for a complementary diagnosis and effective curative treatments.

A novel framework for discovering robust cluster results

Abstract: We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process.

Integration analysis of diverse genomic data using multi-clustering results

Abstract: In modern data mining applications, clustering algorithms are among the most important approaches, because these algorithms group elements in a dataset according to their similarities, and they do not require any class label information. In recent years, various methods for ensemble selection and clustering result combinations have been designed to optimize clustering results. Moreover, conducting data analysis using multiple sources, given the complexity of data objects, is a much more powerful method than evaluating each source separately. Therefore, a new paradigm is required that combines the genome-wide experimental results of multi-source datasets. However, multi-source data analysis is more difficult than single source data analysis. In this paper, we propose a new clustering ensemble approach for multi-source bio-data on complex objects. In addition, we present encouraging clustering results in a real bio-dataset examined using our proposed method.

A Comprehensive Survey of Clustering Algorithms

Abstract: Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

Genetical genomics : the added value from segregation

Abstract: The recent successes of genome-wide expression profiling in biology tend to overlook the power of genetics. We here propose a merger of genomics and genetics into ‘genetical genomics’. This involves expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci. Genetical genomics will combine the power of two different worlds in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.

A Survey of Evolutionary Algorithms for Clustering

Abstract: This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

Gastrointestinal stromal tumours: origin and molecular oncology

Abstract: Gastrointestinal stromal tumours (GISTs) are a paradigm for the development of personalized treatment for cancer patients. The nearly simultaneous discovery of a biomarker that is reflective of their origin and the presence of gain-of-function kinase mutations in these tumours set the stage for more accurate diagnosis and the development of kinase inhibitor therapy. Subsequent studies of genotype and phenotype have led to a molecular classification of GIST and to treatment optimization on the basis of molecular subtype. The study of drug-resistant tumours has advanced our understanding of kinase biology, enabling the development of novel kinase inhibitors. Further improvements in GIST treatment may require targeting GIST stem cell populations and/or additional genomic events.