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- W4378804553 abstract "Deep learning has become a prominent method for learning high-dimensional structured data representation in many domains, and it is gaining traction in bioinformatics. The discovery of transcription factor binding sites (TFBSs) is crucial for understanding the underlying binding mechanisms and cellular functions. To improve the performance of predicting TFBSs, many deep learning and kernel approaches are used for biological sequences. However, each of them has its limitations. In this paper, a convolutional kernel network based on the CKN-seq is extended for in-vitro TFBS prediction experiments and a hybrid framework is proposed, which integrates convolutional kernel network and convolutional neural network (CKNN) to jointly process DNA sequences and their corresponding shape information. The framework integrates DNA sequences and shape features appropriately to better understand protein-DNA binding preferences. Through transcription factor binding experiments, we find that the framework improves prediction accuracy and performs better on small datasets. The hybrid framework not only has the advantages of deep learning in specific tasks but also combines the strengths of high efficiency of the kernel function in small data sets, so it will have broad prospects in small datasets in biological information, intrusion detection, and other fields." @default.
- W4378804553 created "2023-06-01" @default.
- W4378804553 creator A5070995229 @default.
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- W4378804553 date "2023-02-10" @default.
- W4378804553 modified "2023-09-30" @default.
- W4378804553 title "Convolutional Hybrid Kernel Network for in-vitro Transcription Factor Binding Sites" @default.
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- W4378804553 doi "https://doi.org/10.1145/3592686.3592693" @default.
- W4378804553 hasPublicationYear "2023" @default.
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