한빛사논문
- 조회314
Leyi Wei 1,*, Wenjia He 2, Adeel Malik 3, Ran Su 4, Lizhen Cui 5, Balachandran Manavalan 6,*
1computer science from Xiamen University, China.
2School of Software at Shandong University, China.
3Institute of Intelligence Informatics Technology, Sangmyung University, Seoul, Republic of Korea.
4College of Intelligence and Computing, Tianjin University, Tianjin, China.
5School of Software, Shandong University, the Deputy Director of the E-Commerce Research Center and the Director of the Research Center of Software and Data Engineering, Jinan.
6Department of Physiology, Ajou University School of Medicine, Republic of Korea.
*Corresponding author.
Abstract
Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA replication, play essential roles in DNA replication process. Detection of ORIs’ distribution in genome scale is one of key steps to in-depth understanding their regulation mechanisms. In this study, we presented a novel machine learning-based approach called Stack-ORI encompassing 10 cell-specific prediction models for identifying ORIs from four different eukaryotic species (Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana). For each cell-specific model, we employed 12 feature encoding schemes that cover nucleic acid composition, position-specific and physicochemical properties information. The optimal feature set was identified from each encoding individually and developed their respective baseline models using the eXtreme Gradient Boosting (XGBoost) classifier. Subsequently, the predicted scores of 12 baseline models are integrated as a novel feature vector to train XGBoost and develop the final model. Extensive experimental results show that Stack-ORI achieves significantly better performance as compared with their baseline models on both training and independent datasets. Interestingly, Stack-ORI consistently outperforms existing predictor in all cell-specific models, not only on training but also on independent test. Moreover, our novel approach provides necessary interpretations that help understanding model success by leveraging the powerful SHapley Additive exPlanation algorithm, thus underlining the most important feature encoding schemes significant for predicting cell-specific ORIs.
논문정보
- Research article
- 2021년 07월 (BRIC 등록일 2021-08-13)
- 국내(교신)+국외 연구진
- 생명과학 > 응용생물화학
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