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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.10
- 수록면
- 1,848 - 1,852 (5page)
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A promoter is a DNA element that is found surrounding the transcription start site and can regulate gene transcription. The detection of promoters is critical in defining transcription units, examining gene structure, assessing gene regulatory mechanisms, and annotating gene functional information. Many methods for predicting promoters have previously been proposed. However, these approaches’ performance still has to be improved. In this paper, we present HM-Prom, a strong deep learning model for analysing the properties of short eukaryotic promoter sequences and properly recognizing human and mouse promoter sequences. We performed experiments on a benchmark dataset and compared our results to four cutting-edge tools to demonstrate our superiority in 5-fold cross-validation. Furthermore, we put our classifier through its paces on an independent test dataset. Comparative results show that our method outperforms other approaches for recognizing promoters.
목차
Abstract
1. INTRODUCTION
2. MATERIALS AND METHODS
3. RESULT AND DISCUSSION
4. CONCLUSION
REFERENCES
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