| Abstrakt: |
More than seventy million protein sequences exist now, with just around 1% of their functions known. Multi-domain protein prediction is one of the problems in the Bioinformatics field, which was conducted to find the function of proteins. As a result, the researchers attempt to develop algorithms that predict protein functions based on their sequences. We might think of the tremendous rise in sequencing and structural genomics as providing us with a lot of data to expose the complicated sequence, structure, and functional correlations that exist in proteins. However, because of the critical functions that these macromolecules perform in biological mechanisms, acquiring a thorough understanding of their activity has recently emerged as a major challenge. Furthermore, multi-domain protein function prediction approaches are more efficient than methods that are fully processed based on protein sequencing. In this research, we used a model for predicting multi-domain protein function using a Fuzzy Convolutional Neural Network. In this research, we used a novel hybrid CNN and Fuzzy for sequence labelling, specifically to predict the function of the protein. Our hybrid model managed to outperform recent previous studies in terms of accuracy (95.02%) and performance on the UniProtKB dataset, and they showed significant improvements, in the execution time spent in the prediction process. It's crucial to keep your attention on this strategy. Further, study will be needed. [ABSTRACT FROM AUTHOR] |
