Gary D. Bader

brisc is a multithreaded CPU toolkit for single-cell analysis that is faster and more reproducible than existing tools like Scanpy and Seurat.

Deep Lineage uses deep learning to analyze time-series single-cell RNA-sequencing with lineage-tracing data to predict cell fate and gene expression profiles more accurately than existing methods.

A free Meta Quest virtual reality app harmonizes CODEX, Visium, Xenium, volumetric immunofluorescence and electron microscopy data from 5 organs into 4 scale levels using the Human Reference Atlas.

CyChat is a Cytoscape app that translates natural language into reproducible network analysis workflows using a large language model, reducing the need for programming expertise.

This study shows that PLK1 inhibition eradicates quiescent leukemia stem cells through a non-canonical mechanism involving microtubule and endolysosomal disruption, distinct from its canonical mitotic function.

The paper integrates multi-modal data to identify immune subgroups in RCC and suggests immunotherapy strategies based on distinct T cell communication networks.

This study identifies metabolic partnerships between medulloblastoma and macrophages that drive leptomeningeal metastases and suggests targeting the CXCL12-SCARB1-cholesterol axis as a therapeutic strategy.

The paper integrates multiple datasets to create a comprehensive human liver cell atlas, identifying new cell types and associations with demographic factors.

This paper uses Visium HD spatial transcriptomics to create a high-resolution map of human liver cell types and spatial gene expression in adult and pediatric samples.

The paper shows humanized PDX models preserve tumour-specific immune microenvironments, supporting their use for studying immune evasion mechanisms.

The paper maps the multiscale structure of human liver tissue using electron microscopy and deep learning, revealing vascular, cellular, and organelle organization and dynamics.

FLASH-MM improves single-cell differential expression analysis by using a fast linear mixed-effects model that is accurate and efficient.

The paper shows that glioblastoma stem cells form spatial patterns correlated with their transcriptional state, using image analysis to link morphology and gene expression.

The paper constructs a single-cell RNA-seq atlas of the human pediatric liver and identifies age-related gene signatures that differ from adult livers and are relevant to pediatric liver diseases.

The paper constructs a single-nucleus transcriptome atlas of the orbitofrontal cortex in ALS and identifies cell-type-specific transcriptional changes, introducing a deep learning model to analyze alternative polyadenylation.

This study uses single-cell transcriptomics to show that hypoxia in organoid models influences transcriptional subtypes in pancreatic cancer.

The paper constructs a genome-scale genetic interaction network from 4 million double mutants in HAP1 cells, revealing conserved genetic network principles across species and uncovering novel gene functions and cancer dependencies.

This study develops a method to generate functional hepatocytes from human pluripotent stem cells through controlled differentiation and tissue engineering.

RETINA improves EM image segmentation by combining pre-training on CEM500K with a hybrid model, outperforming existing methods on five public datasets.

This study creates a single-nucleus and spatial transcriptomic atlas of the COVID-19 liver, revealing cellular and functional disruptions associated with organ damage.

This paper creates a single-cell atlas of the woodchuck liver to identify cellular programs in health and disease, showing similarities with human HBV infection.

sciRED improves the interpretation of scRNA-seq factor analysis by removing confounding effects and mapping factors to known covariates, revealing biological signals in multiple datasets.

The paper identifies Sox15 as a transcription factor that promotes cone photoreceptor differentiation by inhibiting alternative rod fates in the developing mouse retina.

The paper identifies MALAT1 expression as a simple and effective measure to assess cell quality in scRNA-seq data, improving cell annotation accuracy across species.

This study converts postnatal mouse astrocytes to oligodendrocyte lineage cells using Sox10 or Olig2, supported by single-cell RNA sequencing and computational models.

This paper identifies PRMT5 as a regulator of HTT mRNA splicing and alternative polyadenylation, showing that its inhibition reduces full-length HTT protein levels by promoting premature termination and cleavage.

The paper identifies self-renewing osteochondral stem cells in the postnatal skeleton and compares their transcriptomes across adult, fetal, mouse, and human samples.

This study shows adversarial training improves robustness and interpretability in single-cell RNA-seq cell type classification models.

Fatecode predicts cell fate regulators using scRNA-seq data and deep learning, identifying known master regulators in blood and brain development datasets.