• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.346-355
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• 2002

This study will offer multimodal recognition instead of an existing monomodal bioinfomatics by using face, lips, to improve the accuracy of recognition. Each bioinfomatics vector can be found by the following ways. For a face, the feature is calculated by principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out an equation to calculate the edges of the lips first. Then by using a thinning image and least square method, an equation factor can be drawn. A voice recognition is found with MFCC by using mel frequency. We＇ve sorted backpropagation neural network and experimented with the inputs used above. Based on the experimental results we discuss the advantage and efficiency.

• Proceedings of the PSK Conference
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• 2000.10a
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• pp.61-62
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• 2000

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.107-116
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• 2004

The advent of microarray technologies gives an opportunity to moni tor the expression of ten thousands of genes, simultaneously. Such microarray data can be deteriorated by experimental errors and image artifacts, which generate non-negligible outliers that are estimated by 15% of typical microarray data. Thus, it is an important issue to detect and correct the se faulty probes prior to high-level data analysis such as classification or clustering. In this paper, we propose a systematic procedure for the detection of faulty probes and its proper correction in Genechip array based on multivariate statistical approaches. Principal component analysis (PCA), one of the most widely used multivariate statistical approaches, has been applied to construct a statistical correlation model with 20 pairs of probes for each gene. And, the faulty probes are identified by inspecting the squared prediction error (SPE) of each probe from the PCA model. Then, the outlying probes are reconstructed by the iterative optimization approach minimizing SPE. We used the public data presented from the gene chip project of human fibroblast cell. Through the application study, the proposed approach showed good performance for probe correction without removing faulty probes, which may be desirable in the viewpoint of the maximum use of data information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.5
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• pp.777-782
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• 2002

This study will offer multimodal recognition instead of an existing monomodal bioinfomatics by using facial multi-feature to improve the accuracy of recognition and to consider the convenience of user . Each bioinfomatics vector can be found by the following ways. For a face, the feature is calculated by principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out an equation to calculate the edges of the lips first. Then by using a thinning image and least square method, an equation factor can be drawn. A feature found out the facial parameter distance ratio. We've sorted backpropagation neural network and experimented with the inputs used above. Based on the experimental results we discuss the advantage and efficiency.

• Journal of Photoscience
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• v.9 no.2
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• pp.134-137
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• 2002

PYP consists of a water-soluble apoprotein and 4-hydroxycinnamyl chromophore bound to Cys69 via thiolester linkage, Upon absorption of a photon, the photocycle is initiated, leading to formation of several photo-intermediates. Among them, M intermediate is important to understand the signal transduction mechanism of PYP, because it is a putative signaling state. As well known, the dynamics of a protein is closely correlated with the occurrence of its function. Here we report the results of IO ns molecular dynamics (MD) simulation for the M intermediate in aqueous solution and discuss the characteristic feature of this state from a viewpoint of structural fluctuation.

• Journal of Life Science
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• v.17 no.5 s.85
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• pp.714-718
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• 2007

The high-throughput sequencing of a lot of genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable attention in recent years since exact promoter prediction can give a clue to the elucidation of overall genetic networks. In this study, applications of support vector machine(SVM) to promoter prediction are explored to show a right approaches to discriminate between promoter and non-promoter regions by means of SVM. The results of various experiments show that encoding method, encoding region and learning data constitution can play an important role in the performance of SVM.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.7
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• pp.848-854
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• 2010

The objectives of this study were to estimate genetic parameters for the carcass price and carcass traits contributing to carcass grading and to investigate the influence of each carcass trait on the carcass price using multiple regression and path analyses. Data for carcass traits and carcass prices were collected from March 2003 to January 2009 on steers of Korean cattle raised at private farms. The analytical mixed animal model, including slaughter house-year-month combination, linear and quadratic slaughter age as fixed effects and random animal and residual effects, was used to estimate genetic parameters. The effects of carcass traits on the carcass price were evaluated by applying multiple regression analyses. Heritability estimates of carcass traits were $0.20{\pm}0.08$ for carcass weight (CWT), $0.33{\pm}0.10$ for back fat thickness (BFT), $0.07{\pm}0.05$ for eye-muscle area (EMA) and $0.25{\pm}0.10$ for marbling score (MS), and those of carcass prices were $0.21{\pm}0.10$ for auction price per 1 kg of carcass weight (AP) and $0.13{\pm}0.07$ for total price (CP). Genetic correlation coefficients of AP with CWT and MS were $-0.35{\pm}0.29$ and $0.99{\pm}0.04$, respectively, and those of CP with CWT and MS were $0.59{\pm}0.22$ and $0.39{\pm}0.29$ respectively. If an appropriate adjustment for temporal economic value is available, the moderate heritability estimates of AP and CP might suggest their potential use as the breeding objectives for improving the gross incomes of beef cattle farms. The large genetic correlation estimates of carcass price variables with CWT and MS implied that simultaneous selection for both CWT and MS would be also useful in enhancing income.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.10
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• pp.1387-1393
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• 2014

This study aimed to estimate genetic correlation between carcass grading and retail productivity traits and to estimate the correlated response on retail productivity traits through selection for carcass grading traits in order to assess the efficacy of indirect selection. Genetic parameters were estimated with the data from 4240 Hanwoo steers using mixed models, and phenotypes included carcass weight (CWT), back fat thickness (BFT), eye muscle area (EMA), marbling (MAR), and estimated lean yield percentage (ELP) as the carcass grading traits, and weight and portion of retail cuts (RCW and RCP), trimmed fats (TFW and TFP) and trimmed bones (TBW and TBP) as the lean productivity traits. The CWT had positive genetic correlations with RCW (0.95) and TFW (0.73), but its genetic correlation with RCP was negligible (0.02). The BFT was negatively correlated with RCP (-0.63), but positively correlated with TFW and TFP (0.77 and 0.70). Genetic correlations of MAR with TFW and TFP were low. Among the carcass grading traits, only EMA was positively correlated with both RCW (0.60) and RCP (0.72). The EMA had a relatively strong negative genetic correlation with TFW (-0.64). The genetic correlation coefficients of ELP with RCP, TFW, and TFP were 0.76, -0.90, and -0.82, respectively. These correlation coefficients suggested that the ELP and EMA might be favorable traits in regulating lean productivity of carcass.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.155-162
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• 2008

Biological sequences such as DNA sequences and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological dataset with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with the fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. As the result, the experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

• Journal of Animal Science and Technology
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• v.52 no.2
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• pp.157-164
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• 2010

We investigated the influence of carcass traits on carcass price for Hanwoo cow using multiple regression and path analyses. Data for carcass traits and price were collected on mature Hanwoo cow raised in private farms in Kangwon province that were slaughtered in 2008. A total of 96 animals with the average slaughter age of 51 months were used in the current study. Of the carcass traits studied, marbling score (MAR), dressing percentage (DP), and eye-muscle area (EMA) showed moderate and positive correlations with auction price (AP), while correlation coefficients of meat yield index (MINDEX) with AP were low or even negligible. In multiple regression analyses of AP and carcass price (CP) on cold carcass weight (CWT), back fat thickness (BFT), EMA, MAR, and DP, estimates of $R^2$ were 69.86 and 85.43%, respectively. Partial regression coefficients for CWT, BFT, EMA, MAR and DP were -0.028, -0.106, 0.107, 0.814, and 0.075 in the equation of AP and 0.561, -0.060, 0.083, 0.590, and 0.051 in the equation of CP. In path analyses, MAR's total contribution on the variation of AP was largest (0.667), and the total contributions of MAR and CWT on the variation of CP were 0.403 and 0.397. The current study suggested that marbling was the most influential trait on the variation of auction price, and marbling and cold carcass weight were critical traits on the variation of carcass price.