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• W2765311781 abstract "With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language ProLan to the protein function language GOLan, and build a neural machine translation model based on recurrent neural networks to translate ProLan language to GOLan language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction." @default.
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• W2765311781 date "2017-10-19" @default.
• W2765311781 modified "2023-10-14" @default.
• W2765311781 title "ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network" @default.
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• W2765311781 doi "https://doi.org/10.48550/arxiv.1710.07016" @default.
• W2765311781 hasPublicationYear "2017" @default.
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