• Korea Information Processing Society Review
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• v.6 no.5
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• pp.125-130
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• 1999

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.803-811
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• 2010

Since protein subcellular location and biological function are highly correlated, the prediction of protein subcellular localization can provide information about the function of a protein. In order to enhance the prediction performance, external information other than amino acids sequence information is actively exploited in many researches. This paper compares the prediction capabilities resided in amino acid sequence similarity, protein profile, gene ontology, motif, and textual information. In the experiments using PLOC dataset which has proteins less than 80% sequence similarity, sequence similarity information and gene ontology are effective information, achieving a classification accuracy of 94.8%. In the experiments using BaCelLo IDS dataset with low sequence similarity less than 30%, using gene ontology gives the best prediction accuracies, 93.2% for animals and 86.6% for fungi.

• Environmental Mutagens and Carcinogens
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• v.22 no.3
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• pp.183-186
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• 2002

Hypertension is a multifactorial disease. Both genetic and environmental factors have been implicated in its etiology. Since the impairment of nitric oxide (NOS) production plays an important role in the pathogenesis of hypertension, endothelial nitric oxide synthase (ecNOS) gene is supposed to be a candidate gene of hypertension. Our study group investigated the 27 bp insertion/deletion (Ins/Del) polymorphism of ecNOS gene in 99 Korean normotensives and 98 hypertensives, respectively. There was no significant association with any cardiovascular risk factors as well as hypertension in Koreans. The Ins/Del polymorphism of the ecNOS gene indicated the similar allele distribution among ethnic groups studied. Further studies using larger sample size and subject information is required to describe the general picture of the association between the ecNOS gene polymorphic loci and hypertension

• Animal cells and systems
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• v.12 no.4
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• pp.261-267
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• 2008

The human chromosomes 2, 7, 12 and 17 show genomic homology around Hox gene clusters, is taken as evidence that these paralogous gene families might have arisen from a ancestral chromosomal segment through genome duplication events. We have examined protein data from vertebrate and invertebrate genomes to analyze the phylogenetic history of multi-gene families with three or more of their representatives linked to human Hox clusters. Topology comparison based upon statistical significance and information of chromosome location for these genes examined have revealed many of linked genes coduplicated with Hox gene clusters. Most linked genes to Hox clusters share the same evolutionary history and are duplicated in concert with each other. We conclude that gene families linked to Hox clusters may be suggestion of ancient genome duplications.

• The Korean Journal of Soil Zoology
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• v.7 no.1_2
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• pp.29-34
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• 2002

Hox genes are a family of regulatory gene encoding transcription factor that primarily play a crucial role during development. Several indications suggest their involvement in the control of cell growth and regenration. RT-PCR and souther blot analysis revealed that labial-like gene was increasingly expressed along a spatial gradient in the anterior region of intact worm. During head and tail regeneration, labial-like gene was expressed only in the head region of regenerating body pieces, suggesting that the gene is involved in the anteroposterior patterning in earth-worm. This result could give us information on the significance of Hox genes and the relationship between Hox genes during regeneration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.747-752
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• 2011

Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. A Microarray data of ovarian cancer consists of the expressions of thens of thousands of genes, and there is no systematic procedure to analyze this information instantaneously. In this paper, gene markers are selected by ranking genes according to statistics, popular classification rules - linear discriminant analysis, k-nearest-neighbor and decision trees - has been performed comparing classification accuracy of data selecting gene markers and not selecting gene markers. The Result that apply linear classification analysis at Microarray data set including marker gene that are selected using ANOVA method represent the highest classification accuracy of 97.78% and the lowest prediction error estimate.

• Bioinformatics and Biosystems
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• v.2 no.1
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• pp.12-16
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• 2007

DNA repair means a collection of processes that a cell identifies and corrects damage to genome sequence. The DNA repair processes are important because a genome would not be able to maintain its essential cellular functions without the processes. In this research, we make some gene regulatory networks of DNA repair in S. cerevisiae to know how each gene interacts with others. Two approaches are adapted to make the networks; Bayesian Network and ARACNE. After construction of gene regulatory networks based on the two approaches, the two networks are compared to each other to predict which genes have important roles in the DNA repair processes by finding conserved interactions and looking for hubs. In addition, each interaction between genes in the networks is validated with interaction information in S. cerevisiae genome database to support the meaning of predicted interactions in the networks.

• Genomics & Informatics
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• v.2 no.3
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• pp.131-133
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• 2004

MediScore is an information retrieval system, which helps to search for the set of genes associated with a specific disease or the set of diseases associated with a specific gene. Despite recent improvement of natural language processing (NLP) and other text mining approaches to search for disease associated genes, many false positive results come out due to diversity of exceptional cases as well as ambiguities in gene names. In order to overcome the weak points of current text mining approaches, MediScore introduces statistical normalization based on binomial to normal distribution approximation which corrects inaccurate scores caused by common words not representing genes and interactive rescoring by the user to remove the false positive results. Interactive rescoring includes individual alias scoring for each gene to remove false gene synonyms, referring MEDLINE abstracts, and cross referencing between OMIM and other related information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4442-4466
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• 2016

This paper proposes a privacy-preserving aggregation scheme based on the designed P-Gene (PAPG) for sensor networks. The P-Gene is constructed using the designed erasable data-hiding technique. In this P-Gene, each sensory data item may be hidden by the collecting sensor node, thereby protecting the privacy of this data item. Thereafter, the hidden data can be directly reported to the cluster head that aggregates the data. The aggregation result can then be recovered from the hidden data in the cluster head. The designed P-Genes can protect the privacy of each data item without additional data exchange or encryption. Given the flexible generation of the P-Genes, the proposed PAPG scheme adapts to dynamically changing reporting nodes. Apart from its favorable resistance to data loss, the extensive analyses and simulations demonstrate how the PAPG scheme efficiently preserves privacy while consuming less communication and computational overheads.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.105-113
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• 2007

A gene regulatory network is a network of genes representing how genes influence the activities of other genes. Nowadays from microarray experiments, a large number of measurements on the expression levels of genes are available. One of typical data is the so-called "steady-state model" data measuring the expression levels of other genes after knocking out a particular gene. This paper shows how to reverse engineer a parsimonious gene regulatory network, using these measurement data. Our model considers auto-regulation, which forms a cycle in a genetic network. We also model repressor and enhancer roles of genes. which are not considered in previous known methods.