SDBA: Score Domain-Based Attention for DNA N4-Methylcytosine Site Prediction from Multiperspectives

Abstract

In tasks related to DNA sequence classification, choosing the appropriate encoding methods is challenging. Some of the methods encode sequences based on prior knowledge that limits the ability of the model to obtain multiperspective information from the sequences. We introduced a new trainable ensemble method based on the attention mechanism SDBA, which stands for Score Domain-Based Attention. Unlike other methods, we fed the taskindependent encoding results into the models and dynamically ensembled features from different perspectives using the SDBA mechanism. This approach allows the model to acquire and weight sequence features voluntarily. SDBA is conceptually general and empirically powerful. It has achieved new state-of-the-art results on the benchmark data sets associated with DNA N4-methylcytosine site prediction.

MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing

“…Transformer models and their variants like BERT and GPT offer a novel approach with their multihead attention mechanisms, analyzing sequences or images in parallel and dynamically weighting elements through multihead self-attention mechanisms, thereby comprehending complex patterns and dependencies for better outcomes in related tasks. In tasks related to DNA sequence classification, SDBA has achieved new state-of-the-art results on the benchmark data sets associated with DNA N4-methylcytosine site prediction. In the analysis of mass spectrometry technology, the MSFS method is employed to construct features from the original mass spectrometry data, thereby enhancing the information content of the data set.…”

Editorial: Machine Learning in Bio-cheminformatics

“…Paul et al collectively applied both XGBoost and Shapley values to offer an effective prediction of bacterial promoter with enhanced interpretability. Xin et al employed a domain-based attention mechanism to identify DNA N4-methylcytosine sites. Yang et al discovered human miRNA target sites by learning the interaction patterns between miRNAs and mRNA fragments.…”