• Genomics & Informatics
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• v.10 no.1
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• pp.33-39
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• 2012

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

• Journal of Biomedical Engineering Research
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• v.45 no.2
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• pp.95-100
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• 2024

Vitiligo is a condition characterized by the destruction or dysfunction of melanin-producing cells in the skin, resulting in a loss of skin pigmentation. Facial vitiligo, specifically affecting the face, significantly impacts patients' appearance, thereby diminishing their quality of life. Evaluating the efficacy of facial vitiligo treatment typically relies on subjective assessments, such as the Facial Vitiligo Area Scoring Index (F-VASI), which can be time-consuming and subjective due to its reliance on clinical observations like lesion shape and distribution. Various machine learning and deep learning methods have been proposed for segmenting vitiligo areas in facial images, showing promising results. However, these methods often struggle to accurately segment vitiligo lesions irregularly distributed across the face. Therefore, our study introduces a framework aimed at improving the segmentation of vitiligo lesions on the face and providing an evaluation of vitiligo lesions. Our framework for facial vitiligo segmentation and lesion evaluation consists of three main steps. Firstly, we perform face detection to minimize background areas and identify the face area of interest using high-quality ultraviolet photographs. Secondly, we extract facial area masks and vitiligo lesion masks using a semantic segmentation network-based approach with the generated dataset. Thirdly, we automatically calculate the vitiligo area relative to the facial area. We evaluated the performance of facial and vitiligo lesion segmentation using an independent test dataset that was not included in the training and validation, showing excellent results. The framework proposed in this study can serve as a useful tool for evaluating the diagnosis and treatment efficacy of vitiligo.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.1-7
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• 2013

A personalized recommendation system is a recommendation system that recommends goods to users' taste by using an automated information filtering technology. A collaborative filtering method in this technology is a method that discriminates certain types, which represent similar patterns. Thus, it is possible to estimate the pain strength based on the data of the patients who have the past similar types and extract related conditions according to the similarity in classified patients. A representative method using the Pearson correlation coefficient for extracting the similarity weight may represent inexact results as the sample data is small according to the amount of data. Also, it has a disadvantage that it is not possible to fast draw results due to the increase in calculations as a square scale as the sample data is large. In this paper, the excellency of the intelligence pain nursing intervention u-health system implemented by comparing the scale and similarity group of the sample data for extracting significant data is verified through the evaluation of MAE and Raking scoring. Based on the results of this verification, it is possible to present basic data and guidelines of the pain of patients recognized by nurses and that leads to improve the welfare of patients.