• Genomics & Informatics
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• v.1 no.2
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• pp.65-74
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• 2003

Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.

• Proceedings of the Korean Society of Life Science Conference
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• 2005.04a
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• pp.90-90
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• 2005

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.69-70
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• 2000

The completion of sequencing human genome would motivate us to map millions of human cDNAs onto the unique ruler "genome sequence", in order to identify the exact address of each cDNA together with its exons, its promoter region, and its alternative splicing patterns. The expression patterns of some cDNAs could therefore be associated with these precise gene addresses, which further accelerate studies on mining correlations between motifs of promoters and expressions of genes in tissues. Towards the realization of this goal, we have developed a time-and-space efficient software named SQUALL that is able to map one cDNA sequence of length a few thousand onto a long genome sequence of length thirty million in a couple of minutes on average. Using SQUALL, we have mapped twenty thousand of our Bodymap (http://bodymap.ims.u-tokyo.ac.jp) cDNAs onto the genome sequences of Chr.21st and 22nd. In this talk, I will report the status of this ongoing project.

• Genomics & Informatics
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• v.10 no.1
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• pp.51-57
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• 2012

Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.48-52
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• 2007

As huge amount of literature including biological data is being generated after post genome era, it becomes difficult for researcher to find useful knowledge from the biological databases. Bio-text mining and related natural language processing technique are the key issues in the intelligent knowledge retrieval from the biological databases. We propose a bio-text mining technique for the biologists who find Knowledge from the huge literature. At first, web robot is used to extract and transform related literature from remote databases. To improve retrieval speed, we generate an inverted file for keywords in the literature. Then, text mining system is used for extracting given knowledge patterns and keywords. Finally, we construct a grid computing environment to guarantee processing speed in the text mining even for huge literature databases. In the real experiment for 10,000 bio-literatures, the system shows 95% precision and 98% recall.

• Genomics & Informatics
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• v.17 no.2
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• pp.12.1-12.2
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• 2019

• Genomics & Informatics
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• v.10 no.1
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• pp.44-50
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• 2012

Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time.

• Genomics & Informatics
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• v.7 no.1
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• pp.38-40
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• 2009

The EST Knowledge Integrated System, EKIS (http://ekis.kribb.re.kr), was established as a part of Korea's Ministry of Education, Science and Technology initiative for genome sequencing and application research of the biological model organisms (GEAR) project. The goals of the EKIS are to collect EST information from GEAR projects and make an integrated database to provide transcriptomic and metabolomic information for biological scientists. The EKIS constitutes five independent categories and several retrieval systems in each category for incorporating massive EST data from high-throughput sequencing of 65 different species. Through the EKIS database, scientists can freely access information including BLAST functional annotation as well as Genechip and pathway information for KEGG. By integrating complex data into a framework of existing EST knowledge information, the EKIS provides new insights into specialized metabolic pathway information for an applied industrial material.

• Genomics & Informatics
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• v.6 no.3
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• pp.153-156
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• 2008

Complex diseases such as stroke and cancer have two or more genetic loci and are affected by environmental factors that contribute to the diseases. Due to the complex characteristics of these diseases, identifying candidate genes requires a system-level analysis of the following: gene ontology, pathway, and interactions. A database and user interface, termed StrokeBase, was developed; StrokeBase provides queries that search for pathways, candidate genes, candidate SNPs, and gene networks. The database was developed by using in silico data mining of HGNC, ENSEMBL, STRING, RefSeq, UCSC, GO, HPRD, KEGG, GAD, and OMIM. Forty candidate genes that are associated with cerebrovascular disease were selected by human experts and public databases. The networked cerebrovascular disease gene maps also were developed; these maps describe genegene interactions and biological pathways. We identified 1127 genes, related indirectly to cerebrovascular disease but directly to the etiology of cerebrovascular disease. We found that a protein-protein interaction (PPI) network that was associated with cerebrovascular disease follows the power-law degree distribution that is evident in other biological networks. Not only was in silico data mining utilized, but also 250K Affymetrix SNP chips were utilized in the 320 control/disease association study to generate associated markers that were pertinent to the cerebrovascular disease as a genome-wide search. The associated genes and the genes that were retrieved from the in silico data mining system were compared and analyzed. We developed a well-curated cerebrovascular disease-associated gene network and provided bioinformatic resources to cerebrovascular disease researchers. This cerebrovascular disease network can be used as a frame of systematic genomic research, applicable to other complex diseases. Therefore, the ongoing database efficiently supports medical and genetic research in order to overcome cerebrovascular disease.

• Korean Journal of Plant Taxonomy
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• v.50 no.1
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• pp.8-16
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• 2020

Complete chloroplast genome sequences provide detailed information about any structural changes of the genome, instances of phylogenetic reconstruction, and molecular markers for fine-scale analyses. Recent developments of next-generation sequencing (NGS) tools have led to the rapid accumulation of genomic data, especially data pertaining to chloroplasts. Short reads deposited in public databases such as the Sequence Read Archive of the NCBI are open resources, and the corresponding chloroplast genomes are yet to be completed. The V. dilatatum complex in Korea consists of four morphologically similar species: V. dilatatum, V. erosum, V. japonicum, and V. wrightii. Previous molecular phylogenetic analyses based on several DNA regions did not resolve the relationship at the species level. In order to examine the level of variation of the chloroplast genome in the V. dilatatum complex, raw reads of V. dilatatum deposited in the NCBI database were used to reconstruct the whole chloroplast genome, with these results compared to the genomes of V. erosum, V. japonicum, and three other species in Viburnum. These comparative genomics results found no significant structural changes in Viburnum. The degree of interspecific variation among the species in the V. dilatatum complex is very low, suggesting that the species of the complex may have been differentiated recently. The species of the V. dilatatum complex share large unique deletions, providing evidence of close relationships among the species. A phylogenetic analysis of the entire genome of the Viburnum showed that V. dilatatum is a sister to one of two accessions of V. erosum, making V. erosum paraphyletic. Given that the overall degree of variation among the species in the V. dilatatum complex is low, the chloroplast genome may not provide a phylogenetic signal pertaining to relationships among the species.