Manato Akiyama

RIFT-VAE improves RNA inverse folding by combining grammar-conditioned pretraining with latent-space optimization, achieving high structural recovery on benchmark datasets.

OrthologTransformer improves gene conversion between species by learning evolutionary differences, outperforming codon optimization in PETase expression.

The paper introduces a neural network method for RNA secondary structure prediction that directly infers base-pairing probabilities, improving accuracy for structures with pseudoknots.

This paper develops an informative RNA-base embedding using deep learning to improve RNA structural alignment and clustering, achieving better accuracy than existing methods.

This paper introduces MXfold2, a deep learning method for RNA secondary structure prediction that integrates thermodynamic parameters, achieving higher accuracy and robustness than previous methods.

This paper introduces a new sequence feature called genomic style for bacterial DNA sequence classification, showing it outperforms k-mer frequency in metagenome binning.

This paper introduces an RNA secondary structure prediction algorithm that combines thermodynamic modeling with a structured support vector machine to improve accuracy and reduce overfitting.