🧬 RAG-msCNN: Smarter Prediction of Protein–DNA Binding Sites with AI Power
Understanding where proteins bind to DNA is essential for decoding gene regulation, disease mechanisms, and drug discovery. 🚀 The innovative RAG-msCNN framework brings a fresh AI-driven approach by combining retrieval-augmented generation (RAG) with advanced protein language models and a multi-scale separable convolutional neural network . This hybrid strategy allows the system to learn both evolutionary knowledge and contextual biological patterns, improving how accurately binding sites are identified across complex protein sequences. 🔍 By integrating protein language models , the method captures deep sequence features similar to how NLP models understand human language. At the same time, the RAG component retrieves relevant biological information , enriching predictions with prior knowledge, while the multi-scale CNN detects patterns at different resolutions — from local motifs to global structural signals. 🧠 Together, these layers enhance...
