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- W2030891306 abstract "<ns4:p>This paper presents a novel method to predict the functions of amino acid sequences, based on statistical machine translation programs. To build the translation model, we use the “parallel corpus” concept. For instance, an English sentence “I love apples” and its corresponding French sentence “j’adore les pommes” are examples of a parallel corpus. Here we regard an amino acid sequence like “MTMDKSELVQKA” as one language, and treat its functional description as “0005737 0006605 0019904 (Gene Ontology terms)” as a sentence of another language. We select amino acid sequences and their corresponding functional descriptions in Gene Ontology terms to build the parallel corpus. Then we use a phrase-based translation model to build the “amino acid sequence” to “protein function” translation model. The Bilingual Evaluation Understudy (BLEU) score, an algorithm for measuring the quality of machine-translated text, of the proposed method reaches about 0.6 when neglecting the order of Gene Ontology words. Although its functional prediction performance is still not as accurate as search-based methods, it was able to give the function of amino acid sequences directly and was more efficient.</ns4:p>" @default.
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- W2030891306 date "2013-11-01" @default.
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- W2030891306 title "Translate gene sequence into gene ontology terms based on statistical machine translation" @default.
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- W2030891306 doi "https://doi.org/10.12688/f1000research.2-231.v1" @default.
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