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A Machine Learning Framework For Promoter Identification: Integrating Explainable AI For Genomic Insights by shefat16 is a document available to read on EtoBox.

This study presents a machine learning framework for identifying promoter regions in genomic sequences, addressing limitations of traditional heuristic methods. By integrating explainable AI techniques, the model achieves a classification accuracy of 99.36% while providing insights into the biological relevance of genomic features. The framework enhances both the accuracy and interpretability of promoter identification, contributing to advancements in genomic research and personalized medicine.

Author

shefat16

Language

EN