Researchers combine GPT-Neo with Random Forest to predict Agrobacterium promoters at 97% accuracy
A team from Princess Nourah bint Abdulrahman University developed a promoter prediction method for Agrobacterium tumefaciens that pairs a GPT-Neo transformer with Random Forest feature engineering. The model tokenizes nucleotide sequences into embeddings, which the Random Forest classifier then uses for probabilistic feature extraction. With SMOTE applied to address class imbalance, the system…
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Key points
- GPT-Neo embeddings plus Random Forest achieve 97% accuracy and 0.99 AUC on Agrobacterium promoter prediction
- SMOTE balances classes to equalize sensitivity and specificity
- Published open access in Scientific Reports with CC BY-NC-ND 4.0 license
Read the original at Nature Machine Learning primary sourceOpen source ↗
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Princess Nourah bint Abdulrahman UniversitySpringer NatureGPT-NeoRandom ForestSamee, N.A.Talaat, R.Raza, A.
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