Michiaki Hamada

This paper introduces RLMHelix, an alignment-free method to discover conserved RNA structures in the human genome, identifying new candidates that correlate with RNA-binding protein interactions.

ViroChart maps host responses from virome RNA-seq data to identify phenotypic profiles of viruses, revealing host response patterns in animal infections.

This study identifies age-related erosion of organ-specific endothelial programs and links it to tissue dysfunction across multiple organs.

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.

BWR-finder detects interspersed repeats in large genomes using a BWT-based algorithm, outperforming existing tools in speed and memory usage.

This study introduces a Lomb-Scargle periodogram-based method for detecting differentially expressed genes along pseudotime in trajectory analysis, showing improved performance on complex trajectories.

The paper establishes an HPV18-associated cervical adenocarcinoma model using iRCs and integrates spatial transcriptomics with single-cell multi-omics to identify Gland_A as a key component with specific molecular features.

This study examines RNA synthesis and degradation rates in naked mole-rat and mouse fibroblasts, finding higher degradation rates in aging-related pathways in NMRs.

This paper evaluates blind predictions of dynamic water and ion ensembles around RNA using cryo-EM densities from CASP16, finding that molecular dynamics approaches perform best.

This study introduces a genome editing method to rapidly generate and test driver mutations in target genes, identifying novel resistance mutations in lung cancer.

The paper screens translation-enhancing peptides (TEPs) in E. coli and develops a machine learning model to predict their activity, showing strong correlation with experimental results.

This study identifies ADAR1p110's role in mitosis through phosphorylation at S614, which is essential for its chromatin binding and mitotic progression.

This study improves machine learning models for predicting zoonotic virus infectivity by expanding datasets and using large language models, showing better performance for segmented RNA viruses and post-2018 emerging viruses.

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 uses a hybrid molecular dynamics-deep generative framework to generate cryptic ligand-binding conformations of RNA, demonstrating its effectiveness on the HIV-1 TAR-MV2003 system.

The paper investigates the evolutionary arms race between transposable elements and KRAB-ZFP proteins using ChIP-seq data, revealing new instances and regulatory impacts.

This paper introduces a protein language model to predict antibiotic resistance mechanisms in ARGs, showing improved performance and interpretability on diverse datasets.

REPrise improves de novo interspersed repeat detection by incorporating inexact seeding and other techniques, outperforming RepeatScout in sensitivity for mutated repeats.

The paper identifies directional DNA motifs of DBPs associated with insulator function in human cells and supports the insulator-pairing model.

This study identifies AZIN1 A-to-I editing as a marker of kidney recovery potential following inflammation.

DeepRaccess predicts RNA accessibility using deep learning, achieving high correlation with Raccess and significant speed improvements.

The paper identifies 1,074 semi-extractable RNAs across five human cell lines and characterizes their distribution, interactions, and grouping based on k-mer content.

The paper develops a tool to genotype mobile element variants (MEVs) using short-read data and identifies population-specific MEs linked to disease and gene regulation.

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 introduces LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools that outperform existing methods in speed while maintaining accuracy.

SigTracer improves clone decomposition in tumors by modeling mutation signatures, revealing new insights into mutational processes and their genomic targets.

The paper identifies putative functional RNA elements derived from repeat sequences by analyzing RBP binding patterns using ENCODE eCLIP data.

RaptGen generates aptamers using a variational autoencoder with a profile hidden Markov model decoder, showing effectiveness in creating novel sequences not present in training data.

This study investigates the association between repetitive elements and R-loop formation across species, finding species- and development-specific patterns.

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