Yasuhiro Kojima

CloneVI infers clonal structure from cell states in single-cell RNA-seq and ATAC-seq data, linking mitochondrial DNA mutations to specific cell states.

mmVelo estimates cell state-dependent dynamics across multiple modalities using deep generative modeling, validated on mouse and human data.

Mievformer uses masked self-supervised learning to model microenvironmental and cellular heterogeneity in spatial transcriptomics data, outperforming existing methods in niche clustering and real dataset analysis.

scSurv estimates individual cellular contributions to cancer patient survival using a deep generative model combined with Cox proportional hazards analysis.

STODE reconstructs continuous spatiotemporal dynamics from discrete spatial transcriptomics data, outperforming existing methods in predicting gene expression and spatial coordinates.

DeepKINET estimates single-cell RNA splicing and degradation rates from scRNA-seq data, outperforming existing methods and revealing cellular heterogeneity in post-transcriptional regulation.

LineageVAE reconstructs historical cell states and transcriptomes from scRNA-seq data to infer unobserved progenitor cell states.

ExDyn is a deep generative model that estimates single-cell transcriptome dynamics influenced by extrinsic factors, showing improved accuracy over existing methods.

The paper introduces LincSpectr, a deep generative model that integrates transcriptomic and electrophysiological data to uncover molecular mechanisms underlying time-frequency neural activities.

DeepCOLOR identifies cell-cell interactions by reconstructing spatial colocalization maps from single-cell and spatial transcriptomes in various tissues.

This paper introduces RENGE, a method for inferring gene regulatory networks from time-series scCRISPR data, which outperforms existing methods and produces GRNs consistent with known databases.

The paper identifies ergodicity as the mechanism underlying the reproducibility of embryonic development in zebrafish tailbud by analyzing gene expression and cell motion states.

The paper develops an efficient algorithm to infer gene regulatory networks in Drosophila melanogaster embryogenesis, identifying key regulatory interactions and demonstrating its computational efficiency.