Travis S. Johnson

ARSA: an autonomous research scientist for target nomination in Alzheimer's disease and related dementias

ARSA generates evidence-based target shortlists for Alzheimer's disease and related dementias by integrating natural language research interests with molecular data and cross-cohort evidence.

Optimization and Characterization of SHIP1 Ligands for Cellular Target Engagement and Activity in Alzheimer's Disease Models

This study identifies SHIP1 ligands with brain exposure and in vivo activity in Alzheimer's models, showing target engagement and reduced neuroinflammation.

Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using DEGAS Spatial Smoothing

The paper introduces DEGAS, a method to identify high-risk cells in spatial transcriptomics data by transferring disease attributes to individual cells, validated in cancer and diabetes datasets.

RIPK1 regulates beta-cell fate via actions on gene expression and kinase signaling in a mouse model of beta-cell self-reactivity

This study shows RIPK1 promotes beta-cell death in autoimmune diabetes by affecting gene expression and kinase signaling, suggesting it as a therapeutic target.

Unique and conserved endoplasmic reticulum stress responses in neuroendocrine cells

The paper identifies unique and shared transcriptional responses to ER stress in various endocrine cell types using mRNA sequencing.

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

This study uses deep transfer learning to identify diabetes- and obesity-associated β-cell subpopulations in single-cell RNA-seq data and validates key genes through immunostaining.

Utilizing Genomics to Identify Novel Immunotherapeutic Targets in Multiple Myeloma High-Risk Subgroups

The paper identifies novel immunotherapeutic targets in multiple myeloma high-risk subgroups using genomic data integration and validation.

Target deconvolution of an insulin hypersecretion-inducer acting through VDAC1 with a distinct transcriptomic signature in beta-cells

This paper identifies VDAC1 as the target of SW016789, a hypersecretion inducer, and shows it causes distinct transcriptomic changes in beta-cells compared to ER stress.

A novel deep optimal transport framework reveals prostate cancer risk heterogeneity, Alzheimers disease risk heterogeneity, and myeloma cells associated with a short-term sham bortezomib response and progressive disease

The paper introduces DEGASv2, a deep learning framework for analyzing single-cell and spatial transcriptomics data, revealing disease risk heterogeneity and drug response patterns in multiple conditions.

A novel induced pluripotent stem cell model of schwann cell differentiation reveals NF2-related gene regulatory networks of the extracellular matrix

The paper develops a hiPSC model of Schwann cell differentiation and identifies gene regulatory networks related to NF2 and extracellular matrix organization.

Identify Alzheimer's disease subtypes and makers from multi-omic data of human brain and blood with a subspace merging algorithm

This study integrates multi-omic data from brain and blood to identify AD subtypes and proposes LDLR as a blood biomarker linked to brain gene expression changes.

Enhanced microglial dynamics and paucity of tau seeding in the amyloid plaque microenvironment contributes to cognitive resilience in Alzheimer 's disease

This study examines postmortem brains of AsymAD cases to identify mechanisms of cognitive resilience, finding enhanced microglial dynamics and reduced tau seeding in the amyloid plaque microenvironment.

Diagnostic Evidence Gauge of Spatial Transcriptomics (DEGAS): Using transfer learning to map clinical data to spatial transcriptomics in prostate cancer

The paper introduces DEGAS, a transfer learning framework that maps clinical data to spatial transcriptomics in prostate cancer, identifying transcriptional signatures of carcinogenesis in morphologically normal tissues.

AIscEA: Unsupervised Integration of Single-cell Gene Expression and Chromatin Accessibility via Their Biological Consistency

AIscEA integrates single-cell gene expression and chromatin accessibility data by aligning cell types based on biological consistency, showing improved robustness and accuracy over existing methods.

SPCS: A Spatial and Pattern Combined Smoothing Method of Spatial Transcriptomic Expression

This paper introduces SPCS, a two-factor smoothing method for spatial transcriptomics data that improves separability and biological interpretability compared to one-factor methods.

Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

DEGAS transfers disease information from patients to cells using deep transfer learning, identifying PHF19high myeloma cells linked to progression.

Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains

The paper integrates mouse and human single-cell RNA sequencing data to predict and spatially map cell-type composition in human brain tissue.

BERMUDA: A novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

BERMUDA corrects batch effects in scRNA-seq data and reveals hidden cellular subtypes by transferring information across batches.

LAmbDA: Label Ambiguous Domain Adaption Dataset Integration Reduces Batch Effects and Improves Subtype Detection

LAmbDA reduces batch effects and improves subtype detection by integrating datasets with ambiguous labels across species and studies.