Biomedical subjects

Keun-Joon Park

Publications and source records attributed to Keun-Joon Park.

3 recordsLinked to original sources

PubMed2006

DBTGR: a database of tunicate promoters and their regulatory elements.

The high similarity of tunicates and vertebrates during their development coupled with the transparency of tunicate larvae, their well-studied cell lineages and the availability of simple and efficient transgenesis methods makes of this subphylum an ideal system for the investigation of vertebrate physiological and developmental processes. Recently, the sequencing of two different Ciona genomes has lead to the identification of numerous genes. In order to better understand the regulation of these genes, a database was created containing information on regulation of tunicate genes collected from literature. It includes for instance information regarding the minimal promoter length, the transcription factors involved and their binding sites, as well as the localization of the gene expression. Additionally, binding sites for characterized transcription factors were predicted based on published in vitro recognition sites. Comparison of the promoters of homologous genes in different species is also provided to allow identification of conserved cis elements. At the time of writing, information about 184 promoters, containing 73 identified binding sites and >2000 newly predicted binding sites is available. This database is accessible at http://dbtgr.hgc.jp.

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PubMed2005

Discrimination of outer membrane proteins using support vector machines.

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for dissecting outer membrane proteins (OMPs) from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have developed a method based on support vector machines using amino acid composition and residue pair information. Our approach with amino acid composition has correctly predicted the OMPs with a cross-validated accuracy of 94% in a set of 208 proteins. Further, this method has successfully excluded 633 of 673 globular proteins and 191 of 206 alpha-helical membrane proteins. We obtained an overall accuracy of 92% for correctly picking up the OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins. Furthermore, residue pair information improved the accuracy from 92 to 94%. This accuracy of discriminating OMPs is higher than that of other methods in the literature, which could be used for dissecting OMPs from genomic sequences. AVAILABILITY: Discrimination results are available at http://tmbeta-svm.cbrc.jp.

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PubMed2003

Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs.

MOTIVATION: The subcellular location of a protein is closely correlated to its function. Thus, computational prediction of subcellular locations from the amino acid sequence information would help annotation and functional prediction of protein coding genes in complete genomes. We have developed a method based on support vector machines (SVMs). RESULTS: We considered 12 subcellular locations in eukaryotic cells: chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum, extracellular medium, Golgi apparatus, lysosome, mitochondrion, nucleus, peroxisome, plasma membrane, and vacuole. We constructed a data set of proteins with known locations from the SWISS-PROT database. A set of SVMs was trained to predict the subcellular location of a given protein based on its amino acid, amino acid pair, and gapped amino acid pair compositions. The predictors based on these different compositions were then combined using a voting scheme. Results obtained through 5-fold cross-validation tests showed an improvement in prediction accuracy over the algorithm based on the amino acid composition only. This prediction method is available via the Internet.

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