Tetsu Sakamoto

This paper develops a machine learning framework to classify Leptospira serogroups using genomic data, achieving high accuracy and offering a scalable alternative to traditional serological methods.

Protein Dimension DB provides precomputed protein embeddings and annotations to enable efficient use of large protein language models in functional analysis.

This study analyzes TPE genome sequences to investigate host ancestry and cross-species transmission, finding limited interaction between human and non-human primate infections.

The paper reassesses the taxonomy of Enterococcus casseliflavus and related species using genomic data, identifying three clusters and refining species boundaries.

The paper uses resequencing data from 36 papaya plants to identify genetic variants associated with sex determination, finding 449 variant effects and gene annotations.

The paper identifies genetic patterns in the rfb locus of Leptospira species to propose molecular markers for serogroup classification.

The paper develops an SVM-based method using structural alignment algorithms to detect remote homologs in the SCOPe database, achieving a ROC AUC of 0.9191.

TaxOnTree annotates phylogenetic trees with taxonomic information, enabling visualization of taxonomic distribution of orthologs and paralogs.

The paper characterizes the transcriptome of Arapaima gigas using multi-tissue RNA-sequencing to identify tissue-specific and sex-biased gene expression patterns.

The paper develops an algorithm to complete the NCBI Taxonomy tree by assigning ranks to missing nodes and creating names for unnamed nodes, enabling more complete taxonomic lineage analysis.

This study uses a k-mer-based approach to identify sex-related genomic differences in Arapaima gigas, finding that certain k-mers from repetitive regions show sex-specific variation.