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- W2973006410 abstract "Understanding the specific interactions of transcription factors (TFs) and DNA is essential for comprehending regulatory processes in biological systems. Recently deep learning algorithms have outperformed conventional time-consuming and expensive methods such as ChIP-seq in predicting the sequence specificities of DNA-protein binding. However, because TF binding is a cell-specific behavior, most current deep learning methods build one model for each TF-cell line combination, which leads to problems such as the complexity of maintaining numerous models and the poor prediction performance of some models for cell lines without enough ChIP-seq data. Thus, it is useful to build models with both higher accuracy and wider range of application." @default.
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- W2973006410 date "2019-07-12" @default.
- W2973006410 modified "2023-09-27" @default.
- W2973006410 title "Convolutional Neural Networks Grouped by Transcription Factors for Predicting Protein-DNA Binding Site" @default.
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- W2973006410 doi "https://doi.org/10.1145/3349341.3349448" @default.
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