Kiyoshi Asai

PKProbDesign optimizes thermodynamic folding probability for pseudoknotted RNA targets, achieving high pseudo-joint folding probability on most tested targets.

The paper introduces ID3, a framework for optimizing mRNA sequences using gradient-based methods, balancing multiple properties while preserving amino acid sequences.

The paper introduces Mountain Centroid, a method for representing RNA structural ensembles as single secondary structures, showing improved performance over existing methods in certain metrics.

LinearCapR computes per-nucleotide structural-context probabilities for RNA in linear time without span limits, outperforming existing methods on long-range structures and large RNAs.

StateMap integrates CRISPR fitness and gene expression data to map cancer pathway states, identifying therapeutic vulnerabilities in the Hippo pathway.

Pro2RNA generates species-adapted mRNA coding sequences from protein sequences using a multimodal language model, outperforming existing methods in benchmark tests.

The paper introduces a method to detect readthrough downstream-of-gene transcripts by semi-extractable RNA-seq and identifies stress-inducible, semi-extractable transcripts.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

The paper derives nearest-neighbor parameters for inosine-cytosine RNA pairs using experimental and computational methods, showing their stability compared to canonical base pairs.

The paper introduces RintC, a method for efficiently and accurately decomposing RNA secondary structure probability distributions, validated on 16S rRNA data.

The paper shows that lncRNA-dependent nuclear stress bodies promote intron retention via SR protein phosphorylation during thermal stress recovery.

The paper introduces RNAfamProb and NeoFold to estimate pseudo-probabilities for RNA structural alignment and secondary structures, showing improved accuracy over existing methods.

The paper develops a privacy-preserving protocol for searching chemical compound databases, enabling secure queries without revealing sensitive information.