Keisuke Yamada

This paper uses deep learning to predict relationships between 5' and 3' UTRs, finding that highly related pairs are enriched in genes involved in neural development and have distinct sequence and structural features.

This study develops a computational pipeline to optimize human 5' UTR sequences using a multi-objective framework, addressing up to four conflicting objectives.

The paper introduces BERT-RBP, a model predicting RNA-RBP interactions using BERT architecture, outperforming existing methods and showing sequence-based recognition of transcript regions and RNA structure.

This paper reviews the application of representation learning in biological sequence analysis, focusing on how embedding techniques transform sequences for functional and structural insights.