{mod}raven_python.omics ingests Human Protein Atlas data and turns it into the gene
scores that drive context-specific extraction.
- Proteomics: {func}
raven_python.omics.parse_hpa→ {func}raven_python.omics.hpa_gene_scores. - RNA-seq: {func}
raven_python.omics.parse_hpa_rna→ {func}raven_python.omics.rna_gene_scores.
Both return tidy pandas DataFrames, and the scoring adapters reuse
{func}raven_python.init.score_reactions_from_genes (a single source of truth for the GPR
walk), so omics-derived scores plug straight into
{func}raven_python.init.ftinit / {func}raven_python.init.get_init_model — see the
context-specific modeling guide.
HPA_LEVEL_SCORES exposes the categorical-level → score mapping used for the proteomics
expression levels.