40 results on '"Johnson, Travis S."'
Search Results
2. 302 Diagnostic Evidence Gauge of Spatial Transcriptomics (DEGAS-ST): Using transfer learning to map clinical data to spatial transcriptomics in prostate cancer
3. Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning
4. Developing Cell Surface Target Discovery Pipeline in Metastatic Thymic Epithelial Tumors (mTET)
5. SAT074 Induction Of Insulin Hypersecretion Uncovers Distinctions Between Adaptive And Maladaptive Endoplasmic Reticulum Stress Response In Beta Cells
6. Disease-associated astrocytes and microglia markers are upregulated in mice fed high fat diet
7. Enhanced microglial dynamics and paucity of tau seeding in the amyloid plaque microenvironment contributes to cognitive resilience in Alzheimer’s disease
8. Patient Derived Xenografts Highlight Mouse-Specific Tumor Evolution Patterns and Genomic Diversity of Multiple Myeloma
9. The Pre-Existing T Cell Landscape Is Associated with Response to High Dose Melphalan and Autologous Stem Cell Transplant in Multiple Myeloma
10. Single Cell Multiomic Analysis Reveals Relapsed and Refractory Multiple Myeloma Cells Associated with 1q, TP53, and PHF19 alterations That Affect Subclonal Chromatin Accessibility
11. Spatial Transcriptomic Analysis Reveals Associations between Genes and Cellular Topology in Breast and Prostate Cancers
12. 239-LB: Single-Cell Analysis Identified Inhibitory Neurons Associated with High-Fat Diet in the Bed Nucleus of the Stria Terminalis
13. TSAFinder: exhaustive tumor-specific antigen detection with RNAseq
14. Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease
15. Optimal transport- and kernel-based early detection of mild cognitive impairment patients based on magnetic resonance and positron emission tomography images
16. Differential Gene Expression and Functional Enrichment Analysis of Smoothed PDAC Spatial Transcriptomics Slides
17. SPCS: A Spatial and Pattern Combined Smoothing Method for Spatial Transcriptomic Expression
18. Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s Disease
19. Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
20. Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s Disease
21. Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s Disease
22. Clinical and Molecular Correlates of Tumor Mutation Burden in Non-Small Cell Lung Cancer
23. Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease
24. Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations
25. Pseudogene-gene functional networks are prognostic of patient survival in breast cancer
26. PgenePapers: a novel database and search tools of reported regulatory pseudogenes
27. PgenePapers: a novel database and search tools of reported regulatory pseudogenes
28. A protocol to evaluate RNA sequencing normalization methods
29. Development of a Novel Deep Transfer Learning Framework to Characterize Inter- and Intra-Tumor Heterogeneity in Myeloma Patients
30. A Highly Robust Model for Predicting Outcome of Multiple Myeloma Patients By Inferring Patient-Specific Transcription Factor Activity
31. BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes
32. BERMUDA: A novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes
33. Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression
34. LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection
35. PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers
36. SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer
37. LAmbDA: Label Ambiguous Domain Adaption Dataset Integration Reduces Batch Effects and Improves Subtype Detection
38. Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains
39. Network analysis of pseudogene-gene relationships: from pseudogene evolution to their functional potentials
40. Lack of human cytomegalovirus expression in single cells from glioblastoma tumors and cell lines
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