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Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions by Shujun He; Baizhen Gao; Rushant Sabnis; Qing Sun is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions about?

Much work has been done to apply machine learning and deep learning to genomics tasks, but these applications usually require extensive domain knowledge, and the resulting models provide very limited interpretability. Here, we present the Nucleic Transformer, a conceptually simple but effective and interpretable model architecture that excels in the classification of DNA sequences. The Nucleic Transformer employs self-attention and convolutions on nucleic acid sequences, leveraging two prominent deep learning strategies commonly used in computer vision and natural language analysis. We demonstrate that the Nucleic Transformer can be trained without much domain knowledge to achieve high performance in __Escherichia coli__ promoter classification, viral genome identification, enhancer classification, and chromatin profile predictions.

Who reads Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions?

Author

Shujun He; Baizhen Gao; Rushant Sabnis; Qing Sun

Publisher

American Chemical Society

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)