PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest.

Journal: Interdisciplinary sciences, computational life sciences
Published Date: Apr 30, 2022

Abstract

Promoters short DNA sequences play vital roles in initiating gene transcription. However, it remains a challenge to identify promoters using conventional experiment techniques in a high-throughput manner. To this end, several computational predictors based on machine learning models have been developed, while their performance is unsatisfactory. In this study, we proposed a novel two-layer predictor, called PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning. PredPromoter-MF(2L) was developed based on various deep features learned by a pre-trained deep learning network model and sequence-derived features. Feature selection based on XGBoost was applied to reduce fused features dimensions, and a cascade deep forest model was trained on the selected feature subset for promoter prediction. The results both fivefold cross-validation and independent test demonstrated that PredPromoter-MF(2L) outperformed state-of-the-art methods.

Authors

  • Miao Wang
    Public Affairs Department, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Fuyi Li
    College of Information Engineering, Northwest A&F University, Yangling 712100, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia, National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China, Centre for Research in Intelligent Systems, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Quanzhong Liu
    College of Information Engineering, Northwest A&F University, Yangling 712100, China.
  • Shuqin Li
    College of Information Engineering, Northwest A&F University.

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