5,106 Matching Annotations
  1. Oct 2021
    1. Finally, while Conda is Python-centric to a degree, it is also well-integrated for use with other languages. For example, the base version of Conda includes the C++ standard library.

      renv works really well for projects in R, by the way: https://rstudio.github.io/renv/articles/renv.html

    1. . Division by non-essentials involves dividing a prior differentia-class by means of an unrelated difference. For example, you might first divide animals into winged and wingless, and then winged animals into wild and tame.

      That notion is very similar to the genus-differentia recommended for modelling of computational ontologies: https://philpapers.org/archive/SEPGFW.pdf

  2. May 2021
    1. tissue immunity
    2. stromal–immune interactions
    3. inflammation
    4. oral mucosal homeostasis
    5. atlas
    6. periodontitis
    7. stromal cell
    8. pattern
    9. damage-recognition receptors
    10. neutrophil
    11. fibroblast-expressed genes
    12. genetic mutations
    13. periodontitis
    14. Mendelian
    15. mucosal immunity
    16. neutrophil
    17. stromal
    18. neutrophil
    19. recruitment of leukocytes

      Q106892837

    20. hyper-responsiveness
    21. fibroblast
    22. stromal
    23. recruitment of neutrophils
    24. immune homeostasis
    25. fibroblasts
    26. CXCL1,2,8- expressing epithelial cells
    27. health
    28. gingival mucosa.
    29. disease
    30. tissue homeostasis
    31. immune responsiveness
    32. stromal cells
    33. intercellular communication
    34. immune cell
    35. stromal
    36. transcriptional diversity

      transcriptional diversity

    37. work
    38. periodontitis
    39. health
    40. oral mucosal tissues
    41. human
    42. single-cell transcriptome atlas
    43. cellular level
    44. environment
    45. periodontitis
    46. human microbe-triggered inflammatory diseases
    47. tissue
    48. pathogens
    49. commensals
    50. antigens
    51. barrier tissue
    52. oral mucosa
    1. data
    2. gene regulation
    3. disease heritability
    4. within sc-end5-seq
    5. alternative promoter usage

      Q106878405

    6. chromatin accessibility
    7. aCRE
    8. accessible CRE
    9. tCREs
    10. single cells
    11. tCREs
    12. oligo(dT) priming
    13. random
    14. sc-end5-seq
    15. enhancer RNAs
    16. cost
    17. assay
    18. enhancer activities
    19. quantification
    20. gene expression
    21. transcribed CREs (tCREs)
    22. single-cell RNA-5′end-sequencing
    23. genetic predisposition to diseases
    24. gene regulation
    25. cell-state
    26. cell-type
    27. single cells
    28. promoters
    29. enhancers
    30. cis -regulatory elements (CREs,
    1. disease
    2. homeostasis
    3. development
    4. intestinal cells
    5. inflammation
    6. immune cells
    7. nflammatory bowel disease
    8. early human development
    9. secondary lymphoid tissue formation
    10. cell lineages
    11. systems approach
    12. disease-associated genes
    13. cell-type specific expression
    14. Hirschsprung’s disease
    15. developing enteric nervous system
    16. cell populations
    17. neuronal cell
    18. glial
    19. inflammatory bowel disease
    20. pathogenesis
    21. intestinal tuft cells
    22. function
    23. IgG sensing
    24. colon
    25. cell type
    26. human intestinal tract,
    27. BEST4+ absorptive cells
    28. VDJ analysis
    29. singlecell RNA-seq
    30. anatomical regions,
    31. healthy
    32. pediatric

      pediatric human gut

    33. adult human gut
    34. developing
    35. cell lineages
    36. utero
    37. human intestinal tract
    1. prevention strategies
    2. therapeutic interventions
    3. human body
    4. dataset
    5. biological
    6. SARS-CoV-2 infection
    7. COVID-19 cell atlas
    8. disease severity
    9. genes
    10. cell types
    11. transcriptomic data
    12. GWAS
    13. genetic regions
    14. COVID-19
    15. tissues
    16. COVID-19
    17. viral RNA
    18. tissue host responses
    19. in situ
    20. spatial analysis of RNA profiles
    21. lung tissue
    22. transcriptional programs
    23. host response
    24. cells
    25. endothelial cells
    26. lung mononuclear phagocytic cells
    27. reads
    28. SARS-CoV-2 RNA
    29. lungs
    30. epithelium
    31. regeneration
    32. H1N1 influenza
    33. cells
    34. TP63 + intrapulmonary basal-like progenitor (IPBLP) cells
    35. tissue repair
    36. myofibroblasts
    37. fibrosis
    38. alveolar type 1 (AT1) lung epithelial cells
    39. alveolar type 2 (AT2) differentiation
    40. healthy controls
    41. lung tissue
    42. cell types
    43. cell intrinsic
    44. stromal
    45. immune
    46. epithelial
    47. transcriptional programs
    48. lung
    49. atlases
    50. diseased tissue
    51. healthy
    52. automated cell type annotation
    53. ambient RNA
    54. computational framework
    55. lung regions
    56. spatial RNA profiling
    57. heart tissues
    58. liver
    59. kidney
    60. lung
    61. single-nucleus RNA-Seq
    62. single-cell
    63. COVID-19 biology
    64. cellular maps
    65. specimens
    66. tissue bank
    67. COVID-19
    68. individuals
    69. clinical autopsy
    70. organs
    71. tissues
    72. lethality
    73. symptoms
    74. infection