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• W3161148599 abstract "The identification of enhancers has always been an important task in bioinformatics owing to their major role in regulating gene expression. For this reason, many computational algorithms devoted to enhancer identification have been put forward over the years, ranging from statistics and machine learning to the increasing popular deep learning. To boost the performance of their methods, more features tend to be extracted from the single DNA sequences and integrated to develop an ensemble classifier. Nevertheless, the sequence-derived features used in previous studies can hardly provide the 3D structure information of DNA sequences, which is regarded as an important factor affecting the binding preferences of transcription factors to regulatory elements like enhancers. Given that, we here propose DENIES, a deep learning based two-layer predictor for enhancing the identification of enhancers and their strength. Besides two common sequence-derived features (i.e. one-hot and k-mer), it introduces DNA shape for describing the 3D structures of DNA sequences. The results of performance comparison with a series of state-of-the-art methods conducted on the same datasets prove the effectiveness and robustness of our method. The code implementation of our predictor is freely available at https://github.com/hlju-liye/DENIES." @default.
• W3161148599 created "2021-05-24" @default.
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• W3161148599 date "2021-05-17" @default.
• W3161148599 modified "2023-09-23" @default.
• W3161148599 title "DENIES: A deep learning based two-layer predictor for enhancing the identification of enhancers and their strength with DNA shape information" @default.
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