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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.10
- 수록면
- 1,853 - 1,857 (5page)
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초록· 키워드
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In a variety of cellular and developmental processes, RNA alterations are important. Understanding the distributions of RNA modifications in genome sequences will lead to the discovery of their functions. In the last five years, computational methods for identifying RNA changes have been presented because experimental methods are time consuming and complex. However, both experimental and existing computational approaches have difficulties when it comes to concurrently recognizing changes on various nucleotides. Recently a machine learning based model for simultaneously identifying multiple kinds of RNA modifications was proposed however the neural networks for such problem are not explored yet. To solve this problem, we built a new predictor in this paper that can identify m6A, m5C, and m1A for alterations in Homo sapiens, Mus musculus, and Saccharomyces cerevisiae at the same time. The proposed model uses k-mer encoding scheme to encode the input sequence. The encoded sequence is used by convolution neural network which automatically learns the features and performs classification between modified and unmodified sequences. The 10-fold cross-validation results have exhibited improved results in comparison to the existing state-of-the-art results in literature.
목차
Abstract
1. INTRODUCTION
2. DATASET
3. PROPOSED FRAMEWORK
4. RESULTS AND DISCUSSION
5. CONCLUSION
REFERENCES
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