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- W4378649787 startingPage "107065" @default.
- W4378649787 abstract "The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we designed a method to identify SH2 domain-containing proteins and non-SH2 domain-containing proteins through deep learning technology. Firstly, we collected SH2 and non-SH2 domain-containing protein sequences including multiple species. We built six deep learning models through DeepBIO after data preprocessing and compared their performance. Secondly, we selected the model with the strongest comprehensive ability to conduct training and test separately again, and analyze the results visually. It was found that 288-dimensional (288D) feature could effectively identify two types of proteins. Finally, motifs analysis discovered the specific motif YKIR and revealed its function in signal transduction. In summary, we successfully identified SH2 domain and non-SH2 domain proteins through deep learning method, and obtained 288D features that perform best. In addition, we found a new motif YKIR in SH2 domain, and analyzed its function which helps to further understand the signaling mechanisms within the organism." @default.
- W4378649787 created "2023-05-30" @default.
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- W4378649787 date "2023-08-01" @default.
- W4378649787 modified "2023-10-16" @default.
- W4378649787 title "Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method" @default.
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- W4378649787 doi "https://doi.org/10.1016/j.compbiomed.2023.107065" @default.
- W4378649787 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37267826" @default.
- W4378649787 hasPublicationYear "2023" @default.
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