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Breast Cancer Subtype Classification Using Multi-omics Data Integration Based on Neural Network
Joungmin Choi, Jiyoung Lee, Jieun Kim, Jihyun Kim, Heejoon Chae
http://doi.org/10.5626/JOK.2020.47.9.835
Breast cancer is one of the highly heterogeneous diseases comprising multiple biological factors, causing multiple subtypes. Early diagnosis and accurate subtype prediction of breast cancer play a critical role in the prognosis of cancer and are crucial to providing appropriate treatment for each patient with different subtypes. To identify significant patterns from enormous volumes of genetic and epigenetic data, machine learning-based methods have been adopted to the breast cancer subtype classification. Recently, multi-omics data integration has attracted much attention as a promising approach in recognizing complex molecular mechanisms and providing a comprehensive view of patients. However, because of the characteristics of high dimensionality, multi-omics based approaches are limited in prediction accuracy. In this paper, we propose a neural network-based breast cancer subtype classification model using multi-omics data integration. The gene expression, DNA methylation, and miRNA omics dataset were integrated after preprocessing and the classification model was trained based on the neural network using the dataset. Our performance evaluation results showed that the proposed model outperforms all other methods, providing the highest classification accuracy of 90.45%. We expect this model to be useful in predicting the subtypes of breast cancer and improving patients’ prognosis.
Design of Video Advertisement Analysis via Analysis of Internet Term Sensitivity
Sejin Kim, Jieun Kim, Wonyoung Seong, Yoonhee Kim
http://doi.org/10.5626/JOK.2019.46.9.919
Analysis of the increasing influence of video advertisements via Social Networking Service (SNS) is important in identifying their effects. However, the traditional methods of survey-based analysis are not suitable for measurement of the effectiveness of SNS advertisements that are distributed rapidly via smartphone use and the current system does not consider the sensitivity of users expressed in various forms, such as slang, and emoticons. This study proposes an automated system for the analysis of the effects of video ads via video comments, reflecting the characteristics of short Korean sentences.
This system uses machine learning for the interpretation of Internet terms and compilation of a sentiment dictionary specializing in SNS short sentences. Emoticon, which is used to emphasize the sensitivity of users in comments, is used for sentiment analysis when applied to Korean syntax rules, and the system is designed and implemented for more sophisticated emotional analysis by calculating the emotional values of nouns that are subject to sentiment.
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