Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties

Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional role...

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Bibliographic Details
Published in:International journal of molecular sciences 2022-03, Vol.23 (6), p.3044
Main Authors: Zhu, Huan, Ao, Chun-Yan, Ding, Yi-Jie, Hao, Hong-Xia, Yu, Liang
Format: Article
Language:English
Subjects:
Algorithms
Computational Biology - methods
Computer applications
dihydrouridine
E coli
Eukaryotes
Experiments
Gene expression
Independent sample
Machine learning
nucleotide chemical properties
Nucleotides
oversample
Post-transcription
prediction
random forest
Reproducibility of Results
RNA modification
RNA, Transfer
Software
Support vector machines
Transfer RNA
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Summary:Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional roles. Traditional experimental techniques to identify D are laborious and time-consuming. In addition, there are few computational tools for such analysis. In this study, we utilized eleven sequence-derived feature extraction methods and implemented five popular machine algorithms to identify an optimal model. During data preprocessing, data were partitioned for training and testing. Oversampling was also adopted to reduce the effect of the imbalance between positive and negative samples. The best-performing model was obtained through a combination of random forest and nucleotide chemical property modeling. The optimized model presented high sensitivity and specificity values of 0.9688 and 0.9706 in independent tests, respectively. Our proposed model surpassed published tools in independent tests. Furthermore, a series of validations across several aspects was conducted in order to demonstrate the robustness and reliability of our model.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms23063044