An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches
| Autor: | Yu-Yen Ou, Duong Nguyen, Le Nguyen Quoc Khanh, The-Anh Tran, Dinh-Minh Pham |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
business.industry
Applied Mathematics Deep learning Promoter DNA DNA Methylation Biology Machine learning computer.software_genre Genome Random forest Machine Learning 5-Methylcytosine chemistry.chemical_compound chemistry DNA methylation Genetics Artificial intelligence Promoter Regions Genetic business Gene computer Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19:87-94 |
| ISSN: | 2374-0043 1545-5963 |
| DOI: | 10.1109/tcbb.2021.3082184 |
| Popis: | It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites in promoters is therefore a crucial step in further understanding of the relationship be-tween promoter methylation and the regulation of mRNA gene expression. High throughput identification of DNA 5mC in wet lab is still time-consuming and labor-extensive. Thus, finding the 5mC site of genome-wide DNA pro-moters is still an important task. We compared the effectiveness of the most popular and strong machine learning techniques namely XGBoost, Random Forest, Deep Forest, and Deep Feedforward Neural Network in predicting the 5mC sites of genome-wide DNA promoters. A feature extraction method based on k-mers embeddings learned from a language model were also applied. Overall, the performance of all the surveyed models surpassed deep learning models of the latest studies on the same dataset employing other encoding scheme. Furthermore, the best model achieved AUC scores of 0.962 on both cross-validation and independent test data. We concluded that our approach was efficient for identifying 5mC sites of promoters with high performance. |
| Databáze: | OpenAIRE |
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