• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• KSBB Journal
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• v.18 no.1
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• pp.39-44
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• 2003

A Gene content phylogenetic tree and a 16s rRNA based phylogenetic tree were compared for 33 whole-genome sequenced procaryotes, neighbor joining and bootstrap methods (n=1,000). Ratio of conserved COG (clusters of orthologous groups of proteins) to orthologs revealed that they were within the range of 4.60% (Mezorhizobium loti) or 56.57% (Mycopiasma genitalium). This meant that the ratio was diverse among analyzed procaryotes and indicated the possibility of searching for useful genes. Over 20% of orthologs were independent among the same species. The gene content tree and the 16s rDNA tree showed coincidence and discordance in Archaeabacteria, Proteobacteria and Firmicutes. This might have resulted from non-conservative genes in the gene content phylogenetic tree and horizontal gene transfer. The COG based gene content tree could be regarded as a midway phylogeny based on biochemical tests and nucleotide sequences.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.1044-1049
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• 2006

Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.851-856
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• 2005

The DNA and protein data of diverse species have been daily discovered and deposited in the public archives according to each established format. Database systems in the public archives provide not only an easy-to-use, flexible interface to the public, but also in silico analysis tools of unidentified sequence data. Of such in silico analysis tools, multiple sequence alignment [1] methods relying on pairwise alignment and Smith-Waterman algorithm [2] enable us to identify unknown DNA, protein sequences or phylogenetic relation among several species. However, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST was combined with a clustering tool. Our clustering and annotating tool is summarized as the following steps: (1) construction of suffix tree; (2) masking of cross-matching pairs; (3) clustering of gene sequences and (4) annotating gene clusters by BLAST search. The system was successfully evaluated with 22 gene sequences in the pyrubate pathway of bacteria, clustering 7 clusters and finding out representative common subsequences of each cluster

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2004.05a
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• pp.33-34
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• 2004

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.4-6
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• 2006

Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metaboiic shift data, and yeast sporulation data, shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2004.06a
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• pp.258-265
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• 2004

Numerous genes of Escherichia coli have been shown to growth phase-dependent expression throughout growth. The global patterns of growth phase-dependent gene expression of E. coli throughout growth using oligonucleotide microarrays containing a nearly complete set of 4,289 annotated open reading frames. To determine the change of gene expression throughout growth, we compared RNAs taken from timecourses with common reference RNA, which is combined with equal amount of RNA pooled from each time point. The hierarchical clustering of the conditions in accordance with timecourse expression revealed that growth phases were clustered into four classes, consistent with known physiological growth status. We analyzed the differences of expression levels at genome level in both exponential and stationary growth phase cultures. Statistical analysis showed that 213 genes are shown to, growth phase-dependent expression. We also analyzed the expression of 256 known operons and 208 regulatory genes. To assess the global impact of RpoS, we identified 193 genes coregulated with rpoS and their expression levels were examined in the isogenic rpoS mutant. The results revealed that 99 of 193 were novel RpoS-dependent stationary phase-induced genes and the majority of those are functionally unknown. Our data provide that global changes and adjustments of gene expression are coordinately regulated by growth transition in E. coli.

• Korean Journal of Veterinary Research
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• v.43 no.2
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• pp.219-230
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• 2003

Porcine epidemic diarrhea virus(PED), a member of Coronaviridea, is the etiological agent of enteropathogenic diarrhea in swine. The purpose of this study was to investigate genetic characteristic of PEDV isolated in Korea. Nucleocapsid(N) gene and membrane (M) gene of recent Korean PEDV strains isolated in 2001 were amplified, cloned, sequenced and analyzed. N gene of seven Korean PEDV field isolates bad 94.5% to 99.4% nucleotide and 92.4% to 99.4% amino acid sequence homology each other. Nucleotide and amino acid sequences of Korean field PEDVs were different from published foreign PEDVs, showing 95.1% to 98.0% nucleotide and 93.5% to 97.6% amino acid sequence homology. By phylogenetic tree analysis on based nucleotide sequences, PEDVs were clustered into four groups. By phylogenetic tree analysis based on amino acid sequences. PEDVs were clustered into five groups. M gene of our Korean PEDV field isolates had 99.6% to 100% nucleotide and 98.7% to 100% amino acid sequence homology each other. Nuclotide and amino acid sequences of Korean field PEDVs were different from published foreign PEDVs, showing 98.5% to 98.8% nucleotide and 97.3% to 97.8% amino acid sequence homology. By phylogenetic tree analysis based on nucleotide and amino acid sequences, PEDVs were clustered into two groups which were Korean PEDV isolate group and foreign PEDV isolate group.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.210-210
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• 2022

Silybum marianum is an annual or biennial plant from the Asteraceae family. It can grow in low-nutrient soil and drought conditions, making it easy to cultivate. From the seed, a specialized plant metabolite called silymarin (flavonolignan complex) is produced and is known to alleviate the liver from hepatitis and toxins damages. To infer the phylogenetic placement of a Korean milk thistle, we conducted a chloroplast assembly and annotation following by a comparison with existing Chinese reference genome (NC_028027). The chloroplast genome structure was highly similar with an assembly size of 152,642 bp, an 153,202 bp for Korean and Chinese milk thistle respectively. Moreover, there were similarities at the gene level, coding sequence (n = 82), transfer RNA (n = 31) and ribosomal RNA (n = 4). From all coding sequences gene set, the phylogenetic tree inference placed the Korean cultivar into the milk thistle clade; corroborating the expected tree. Moreover, an investigation the tree based only on the ycf1 gene confirmed the same tree; suggesting that ycf1 gene is a potential marker for DNA barcoding and population diversity study in milk thistle genus. Overall, the provided data represents a valuable resource for population genomics and species-centered determination since several species have been reported in the Silybum genus.

• Journal of Microbiology and Biotechnology
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• v.16 no.5
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• pp.748-756
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• 2006

We propose a genome-scale clustering approach to identify whole genome relationships using the functional groups given by the Kyoto Encyclopedia of Genes and Genomes Orthology (KO) database. The metabolic capabilities of each organism were defined by the number of genes in each functional category. The archaeal, bacterial, and eukaryotic genomes were compared by simultaneously applying a two-step clustering method, comprised of a self-organizing tree algorithm followed by unsupervised hierarchical clustering. The clustering results were consistent with various phenotypic characteristics of the organisms analyzed and, additionally, showed a different aspect of the relationship between genomes that have previously been established through rRNA-based comparisons. The proposed approach to collect and cluster the metabolic functional capabilities of organisms should make it a useful tool in predicting relationships among organisms.