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Using k-mer Embeddings Learned from a Skip-Gram Based Neural Network as Effective Feature Representation for Building a Cross-Species Prediction Model to Identify DNA N6-Methyladenine Sites in Plant Genomes

2021-06-30

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<title>Abstract</title> Identification of DNA N6-methyladenine sites has been a very active topic of computational biology due to the unavailability of suitable methods to identify them accurately, especially in plants. Substantial results were obtained with a great effort put in extracting, heuristic searching, or fusing a diverse types of features, not to mention a feature selection step. We considered DNA,...

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Literature Corpus work: b3546144-5785-5a2c-bee6-37d311551b4b
DOI: 10.21203/rs.3.rs-553304/v1

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Using k-mer Embeddings Learned from a Skip-Gram Based Neural Network as Effective Feature Representation for Building a Cross-Species Prediction Model to Identify DNA N6-Methyladenine Sites in Plant GenomesDOI 10.21203/rs.3.rs-553304/v1Read

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