• Journal of Nutrition and Health
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• v.57 no.1
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• pp.75-87
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• 2024

Purpose: Dietary habits are strongly related to the symptoms of people with irritable bowel syndrome (IBS). Therefore, personalized nutrition management can help reduce symptoms and improve the quality of life of people with IBS. This study assessed the effectiveness of a personalized web-based nutrition management based on the types of food that trigger IBS symptoms. Methods: Sixty Korean adults with IBS according to Rome IV criteria in their 20s and 30s were enrolled in this study. The data from the final 49 patients who completed a three-month personalized nutrition intervention were analyzed. The general information, anthropometry, dietary intake survey, and gut microbiota were examined pre and post-intervention. The gut microbiota analysis included the relative abundance and the Shannon index. The food intake was recorded for two days for personalized nutrition education, followed by three months of personalized nutrition intervention. Statistical analysis was performed using the Wilcoxon signed-rank test in SPSS 26.0, with the significance set to p < 0.05. Results: The relative abundance of the gut microbiota changed after personalized nutrition management, with a significant decrease in the presence of Veillonella (p = 0.048). Furthermore, when the gut microbiota was analyzed according to the type of food that triggers symptoms, the diversity was increased significantly in the high fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) type (p = 0.031) and FODMAPs-containing gluten-type personalized nutrition intervention types (p < 0.001). Conclusions: Gut microbial diversity and gut microbiota distribution changed after using web-based personalized nutrition management. Hence, personalized nutrition management that considers trigger foods may improve IBS symptoms.

• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.383-386
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• 2001

In this paper, we propose a new personalized information service method using multiple regression analysis for Internet shopping malls. We then apply the proposed method to Internet bookstore, and compare personalized Internet bookstore providing personalized book information with ordinary Internet bookstore providing random book information. Experiment results show that the personalization of Internet bookstore increases satisfaction and purchasing intent of customers.

• Journal of Families and Better Life
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• v.24 no.2 s.80
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• pp.15-26
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• 2006

The purposes of this study were (1) to investigate children's personalized inferences of characters emotional reactions depending on character's personality trait, emotional situation, children's age and gender, (2) to investigate children's personalized inferences of character's behavioral reactions depending on character's personality trait, emotional situation, children's age and gender, (3) to investigate differences between children's personalized inferences of character's emotional reaction and that of character's behavioral reactions. The subjects were 103 children from three age groups (thirty-four 3-year-olds, thirty-three 5-year-olds and thirty-six 7-year-olds). The statistical methods adopted for the data analysis were frequency, percentile, mean, standard deviation, repeated measure ANOVA and paired t-test. The result showed that there were significant differences in children's personalized inferences of character's emotional reaction depending on character's personality trait, emotional situation and their age. There were significant differences in children's personalized inferences of character's behavioral reaction depending on children's age and gender. There were significant differences between personalized inferences of character's emotional reaction and behavioral reactions.

• Hwankyungkyoyuk
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• v.18 no.3 s.28
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• pp.45-58
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• 2005

The Purpose of this study is to help children practice environmentally friendliness by promoting Personalized Environment. Therefore, implemented the project 'Environment' that included natural and social environment in the area that had high potential energy for environmental education, it was explored how the project affects the personalized environment of students. The project 'Environment' was implemented, usually focused on field trips. 35 study subjects were selected from 2 classes in D elementary school in D city The study was conducted from march 2004 to November 2004. Research material was collected from participant observation, in-depth interview, products from teaching-learning process, diary, and video record Ethnographic research was used. Study data were analyzed 3 times. 1st analysis was needed to collect data related to students' senses of and interests in environment, and to shape the framework related to the theme. End analysis was used to categorize the main factors such as knowledge understanding, function, value attitude, action, which all affect the personalized environment of students. In 3rd analysis, it was examined what affected the personalized environment of children and the related factors were logically, objectively inferred and analyzed. The results of the study were as follows: First, the project 'Environment' developed children's knowledge and understanding about environment. Second, the children formed proper values and attitudes towards environment. Third, the children came to develop their learning abilities on environment. Fourth, the children came to have environmentally friendliness in everyday lives. Consequently, the project 'Environment' helped children to have Personalized Environment.

• The Journal of Information Systems
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• v.29 no.2
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• pp.243-263
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• 2020

Purpose The purpose of this study is to analyze the causal relationship between user satisfaction, expected satisfaction, quality of service, perceived value, and expertise that should be emphasized in personalized medical information services for the development of personalized medical information services based on big data analysis and the spread of their demand. Design/methodology/approach This study established research models and hypotheses on the basis of the theory of reuse intent, and applied the PLS methodology for analysis, the factors that make it applicable to personalized medical services in the theory of service quality and satisfaction. Findings According to the empirical analysis result, this study confirmed that it can be seen that the expertise, perceived value, and quality of medical services did not directly affect the user's intention to reuse, but formed a direct causal relationship through variables such as whether they met expectations.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.173-177
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• 2009

With the advent of light-weight daily physiological signal monitoring sensors, intelligent inference and analysis method for physiological signal monitoring application, commercialized products and services are released. However, practical constraints still remain for daily physiological signal monitoring. Most devices provide rough health check function and analyze with randomly sampled measurements. In this work, we propose the probabilistic modeling of physiological signal analysis. This model represent the relationship between previous user measurement (history), other group`s type, model and current observation. From the experiment, we found that the personalized analysis with long term regular data shows reliable result and reduces the analyzing errors. In addition, participants agree that the personalized analysis shows reliable and adaptive information than other standard analysis method.

• Genomics & Informatics
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• v.18 no.4
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• pp.46.1-46.4
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• 2020

We developed the BaSDAS (Barcode-Seq Data Analysis System), a GUI-based pooled knockout screening data analysis system, to facilitate the analysis of pooled knockout screen data easily and effectively by researchers with limited bioinformatics skills. The BaSDAS supports the analysis of various pooled screening libraries, including yeast, human, and mouse libraries, and provides many useful statistical and visualization functions with a user-friendly web interface for convenience. We expect that BaSDAS will be a useful tool for the analysis of genome-wide screening data and will support the development of novel drugs based on functional genomics information.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.113-121
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• 2019

The personalized meal service is being developed to prevent and alleviate illnesses according to the individual's health condition. However, the current meal does not provide a fully customized service to individuals and a diet that meets the consumer's information needs. The cause is the lack of information on the ingredients and the difficulty of comparative analysis between the materials. Therefore, in this study, we propose basic analysis process for basic information acquisition and database construction for food composition before providing personalized food. In this study, we investigated the content of carbohydrate, reducing sugar and protein as basic components of Grifola frondosa and investigate the content of polyphenol as a biological active ingredients. Respectively. Studies on the hypoglycemic effect of the diabetic rat model have been carried out in relation to the prevention of diseases. Based on the results of this study, it is also possible to obtain information on the basic ingredients of the food and to analyze the information on the content and activity of the biological active ingredients. Using animal models, information on disease prevention and mitigation was also available. The process introduced in this study is applied to various food materials, accumulating data, and utilizing Database, this results will be an excellent tool for providing more efficient service by providing a proper dietary composition for consumers.

• Genomics & Informatics
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• v.15 no.1
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• pp.11-18
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• 2017

Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPseeker. 'dada2' performs trimming of the high-throughput sequencing data. 'QuasR' and 'mosaics' perform quality control and mapping of the input reads to the reference genome and peak calling, respectively. Finally, 'ChIPseeker' performs annotation and visualization of the called peaks. This workflow runs well independently of operating systems (e.g., Windows, Mac, or Linux) and processes the input fastq files into various results in one run. R code is available at github: https://github.com/ddhb/Workflow_of_Chipseq.git.