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
. 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
tissue immunity
stromal–immune interactions
inflammation
oral mucosal homeostasis
atlas
periodontitis
stromal cell
pattern
damage-recognition receptors
neutrophil
fibroblast-expressed genes
genetic mutations
periodontitis
Mendelian
mucosal immunity
neutrophil
stromal
neutrophil
recruitment of leukocytes
Q106892837
hyper-responsiveness
fibroblast
stromal
recruitment of neutrophils
immune homeostasis
fibroblasts
CXCL1,2,8- expressing epithelial cells
health
gingival mucosa.
disease
tissue homeostasis
immune responsiveness
stromal cells
intercellular communication
immune cell
stromal
transcriptional diversity
transcriptional diversity
work
periodontitis
health
oral mucosal tissues
human
single-cell transcriptome atlas
cellular level
environment
periodontitis
human microbe-triggered inflammatory diseases
tissue
pathogens
commensals
antigens
barrier tissue
oral mucosa
data
gene regulation
disease heritability
within sc-end5-seq
alternative promoter usage
Q106878405
chromatin accessibility
aCRE
accessible CRE
tCREs
single cells
tCREs
oligo(dT) priming
random
sc-end5-seq
enhancer RNAs
cost
assay
enhancer activities
quantification
gene expression
transcribed CREs (tCREs)
single-cell RNA-5′end-sequencing
genetic predisposition to diseases
gene regulation
cell-state
cell-type
single cells
promoters
enhancers
cis -regulatory elements (CREs,
disease
homeostasis
development
intestinal cells
inflammation
immune cells
nflammatory bowel disease
early human development
secondary lymphoid tissue formation
cell lineages
systems approach
disease-associated genes
cell-type specific expression
Hirschsprung’s disease
developing enteric nervous system
cell populations
neuronal cell
glial
inflammatory bowel disease
pathogenesis
intestinal tuft cells
function
IgG sensing
colon
cell type
human intestinal tract,
BEST4+ absorptive cells
VDJ analysis
singlecell RNA-seq
anatomical regions,
healthy
pediatric
pediatric human gut
adult human gut
developing
cell lineages
utero
human intestinal tract
prevention strategies
therapeutic interventions
human body
dataset
biological
SARS-CoV-2 infection
COVID-19 cell atlas
disease severity
genes
cell types
transcriptomic data
GWAS
genetic regions
COVID-19
tissues
COVID-19
viral RNA
tissue host responses
in situ
spatial analysis of RNA profiles
lung tissue
transcriptional programs
host response
cells
https://www.wikidata.org/w/index.php?title=Q7868&action=info
endothelial cells
lung mononuclear phagocytic cells
reads
SARS-CoV-2 RNA
lungs
epithelium
regeneration
H1N1 influenza
cells
TP63 + intrapulmonary basal-like progenitor (IPBLP) cells
tissue repair
myofibroblasts
fibrosis
alveolar type 1 (AT1) lung epithelial cells
alveolar type 2 (AT2) differentiation
healthy controls
lung tissue
cell types
cell intrinsic
stromal
immune
epithelial
transcriptional programs
lung
atlases
diseased tissue
healthy
automated cell type annotation
ambient RNA
computational framework
lung regions
spatial RNA profiling
heart tissues
liver
kidney
lung
single-nucleus RNA-Seq
single-cell
COVID-19 biology
cellular maps
specimens
tissue bank
COVID-19
individuals
clinical autopsy
organs
tissues
lethality
symptoms
infection
viruses
lethal SARS-CoV-2 infection
molecular dynamics
mortality
COVID-19