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IEEE/ICACT20250017 Slide.08        [Big Slide]       [YouTube]	Oral Presentation
	Recent research has made significant progress in this area: In 2022, Sturtz and colleagues achieved high accuracy using CNN and LSTM models on the MabCampath scaffold dataset. Their k-mer sequence-based approach has limitations for large gaps or scaffolds with errors. In 2023, Sturtz et al. introduced a convolutional denoising autoencoder model achieving 100% gap filling accuracy and 95.32% full sequence prediction without an iterative process. In 2024, Badal et al. achieved 100% gap filling accuracy using different machine learning algorithms on the Mabcampath scaffold data. Most recently, Qingge et al. developed generative AI models including transformer and GPT-2 architectures for protein scaffold filling, which outperform previous models with very high accuracy.

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