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pLM-DBPs: Enhanced DNA-Binding Protein Prediction in Plants Using Embeddings From Protein Language Models
Abstract DNA-binding proteins (DBPs) play critical roles in gene regulation, development, and environmental response across various species, including plants, animals, and microorganisms. While various machine learning and deep learning models have been developed to distinguish DNA-binding proteins (DBPs) from non-DNA-binding proteins (NDBPs), most available tools have focused on human and mouse datasets.
pLM-DBPs: Enhanced DNA-Binding Protein Prediction in Plants Using Embeddings From Protein Language Models
Abstract DNA-binding proteins (DBPs) in plants play critical roles in gene regulation, development, and environmental response. While various machine learning and deep learning models have been developed to distinguish DBPs from non-DNA-binding proteins (NDBPs), most of the available tools have focused on human and mouse datasets, resulting in sub-optimal performance for plant-based DBP prediction.
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