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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
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- 학술대회자료
- 저자정보
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- 한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2023 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.14 No.1
- 발행연도
- 2023.1
- 수록면
- 61 - 65 (5page)
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초록· 키워드
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MicroRNAs(miRNA) can regulate the protein expression level of gene in the human body. And as miRNAs have been recently revealed that those are closely relatde to the cause of disease, miRNAs are being very actively in the fields of biology and pharmacogenomics. Research to find genes related to microRNAs is of great help in understanding the mechanisms of complex microRNAs. In addition, studies that define genes related to specific microRNAs can make a significant contribution to predicting diseases related to microRNAs, and can also contribute to predicting to predicting Drugs related to microRNAs. But revealing microRNA-related genes through wet experiments(in-vivo, traditional method) requires a lot of time and cost and is not efficient. In order to overcome these problems, recent studies are being conducted to predict the relevance through deep learning models.
This study presents a method for predicting association between microRNAs and genes through a Deep learning model. First of all, it is important to set a negative data set to train association-prediction DL model. Since it is difficult to define absolute negative relationships with biological data, very few data sets of negative relationships have been opende. Togenerate Negative data set, we present our elaborate criteria by distance(between miRNAs and genes) base method. Then we reconstruct negative dataset by our elaborate method. After positive and negative data are equally constructed, we extract miRNA-gene feature by Autoencoder. Then concatenate feature vector to origin data. The concatenated data is fed to LSTM model training. Our miRNA-gene association prediction model shows the performance of 'AUC: 0.9581' and shows the best performance among studies using same microRNA-gene association data set.
This study presents a method for predicting association between microRNAs and genes through a Deep learning model. First of all, it is important to set a negative data set to train association-prediction DL model. Since it is difficult to define absolute negative relationships with biological data, very few data sets of negative relationships have been opende. Togenerate Negative data set, we present our elaborate criteria by distance(between miRNAs and genes) base method. Then we reconstruct negative dataset by our elaborate method. After positive and negative data are equally constructed, we extract miRNA-gene feature by Autoencoder. Then concatenate feature vector to origin data. The concatenated data is fed to LSTM model training. Our miRNA-gene association prediction model shows the performance of 'AUC: 0.9581' and shows the best performance among studies using same microRNA-gene association data set.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. METHODS
Ⅲ. Experiments
Ⅳ. Conclusions
REFERENCES
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