• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.209-214
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• 2005

The use of protein interaction data is highly reliable for predicting functions to proteins without function in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and $\chi^2-statistics$ methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and $\chi^2-statistics$ methods.

• Genomics & Informatics
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• v.21 no.2
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• pp.25.1-25.12
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• 2023

Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by Mycobacterium tuberculosis (MTB) is the world's first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein's anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.

• Journal of Biosystems Engineering
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• v.23 no.3
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• pp.253-258
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• 1998

The part I was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Infrared(NIR) Reflectance analyzer. The purpose of this study(part II) is to measure fundamental data required for the prediction of rice quality, and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undried paddy powder by using Near Infrared(NIR) Reflectance analyzer. The results of this study were summarized as follows : The predicted values of protein contents obtained from the undried paddy powder were well correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to the lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.06b
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• pp.171-177
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• 1998

The part Ⅰ was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Unfrared (NIR) Reflectance analyzer. The purpose of this study(part Ⅱ) is to measure fundamental data required for the prediction of rice quality , and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undreid paddy powder by using Near Infrared (NIR) Reflectance analyzer. The results of this study were summarized as follows . The predicted values of protein contents obtained from the undried paddy powder were will correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to be lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.679-688
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• 2003

Motif databases are used in the function and structure prediction of proteins which appear on new and rapid release of raw data from genome sequencing projects. Recently, the frequency of use about these databases increases continuously. However, existing motif databases were developed and extended independently and were integrated mainly by using a web-based cross-reference, thus these databases have a heterogeneous search result problem, a complex query process problem and a duplicate database entry handling problem. Therefore, in this paper, we suppose physical motif resource integration and describe the integrated search method about a family-based protein prediction for solving above these problems. Finally, we estimate our implementation of the motif integration database and prediction system for predicting protein motifs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2562-2570
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• 2014

One of the important hints for inferring the function of unknown proteins is the knowledge about protein subcellular localization. Recently, there are considerable researches on the prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular localization. In this paper, label power-set classification is improved for the accurate prediction of multiple subcellular localization. The predicted multi-labels from the label power-set classifier are combined with their prediction probability to give the final result. To find the accurate probability estimates of multi-classes, this paper employs pair-wise comparison and error-correcting output codes frameworks. Prediction experiments on protein subcellular localization show significant performance improvement.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.427-435
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• 2005

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However, most of the techniques still have many limitations to be used in real fields. They usually suffer from a low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Probability equations are devised and proposed in the framework of domain combination based protein interaction prediction method. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the precision of the prediction in case of the protein pair group having the matching PIP(Primary Interaction Probability) values in the interacting or non interacting PIP distributions.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.12
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• pp.1674-1680
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• 2004

The objective of this study was to compare the Dutch DVE/OEB system and the NRC-2001 model in the prediction of supply of protein to dairy cows from processed field tick beans. Comparisons were made in terms of 1) ruminally synthesized microbial CP, 2) truly absorbed protein in the small intestine, and 3) degraded protein balance. The results showed that the predicted values from the DVE/OEB system and the NRC-2001 model had significant correlations with high R (>0.90) values. However, using the DVE/OEB system, the overall average microbial protein supply based on available energy was 16% higher and the truly absorbed protein in the small intestine was 9% higher than that predicted by the NRC-2001 model. The difference was also found in the prediction of the degraded protein balances (DPB), which was 5% lower than that predicted based on data from the NRC-2001 model. These differences are due to considerably different factors used in calculations in the two models, although both are based on similar principles. It need to mention that this comparison was based on the limited data, the full comparison involving various types of concentrate feeds will be investigated in the future.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.5
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• pp.580-585
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• 1998

The study was conducted to determine if variation in protein yield can be explained by expressions of early lactation somatic cell score (SCS) and if prediction can be improved by including SCS among the predictors. A data set was prepared (n = 663,438) from Wisconsin Dairy Improvement Association (USA) records for protein yield with sample days near 20. Stepwise regression was used requiring F statistic (p < .01) for any variable to stay in the model. Separate analyses were run for 12 combinations of four seasons and first three parities. Selection of SCS variables was not consistent across seasons or lactations. Coefficients of detennination ($R^2$) ranged from 51 to 61% with higher values for earlier lactations. Including any expression of SCS in the prediction equations improved $R^2$ by < 1 %. SCS was associated with milk yield on the sample day, but the association was not strong enough to improve the prediction of future yield when other expressions of milk yield were in the model.