Abstrait

EnhancerBD identifing sequence feature-identify enhancer

Yi Wang

Deciphering the non-coding language of DNA is one of the fundamental questions in genomic research. Previous bioinformatics methods often struggled to capture this complexity, especially in cases of limited data availability. Enhancers are short DNA segments that play a crucial role in biological processes, such as enhancing the transcription of target genes. Due to their ability to be located at any position within the genome sequence, accurately identifying enhancers can be challenging. We presented a deep learning method (enhancerBD) for enhancer recognition. We extensively compared the enhancerBD with previous 18 state-of-the-art methods by independent test. Enhancer-BD achieved competitive performances. All detection results on the validation set have achieved remarkable scores for each metric. It is a solid state-of-the-art enhancer recognition software. In this paper, I extended the BERT combined DenseNet121 models by sequentially adding the layers Global Average Pooling2D, Dropout, and a Rectified Linear Unit (ReLU) activation function. This modification aims to enhance the convergence of the model's loss function and improve its ability to predict sequence features. The improved model is not only applicable for enhancer identification but also for distinguishing enhancer strength. Moreover, it holds the potential for recognizing sequence features such as lncRNA, microRNA, insultor, and silencer.