gbanyan
8aa66987f8
- Create LiteratureRecord pydantic model with context-specific counts - Implement PubMed query via Biopython Entrez with rate limiting (3/sec default, 10/sec with API key) - Define SEARCH_CONTEXTS for cilia, sensory, cytoskeleton, cell_polarity queries - Implement evidence tier classification: direct_experimental > functional_mention > hts_hit > incidental > none - Implement quality-weighted scoring with bias mitigation via log2(total_pubmed_count) normalization - Add biopython>=1.84 dependency to pyproject.toml - Support checkpoint-restart for long-running PubMed queries (estimated 3-11 hours for 20K genes)
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
"""Tissue expression evidence layer for Usher-relevant tissues.
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This module retrieves expression data from:
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- HPA (Human Protein Atlas): Tissue-level RNA/protein expression
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- GTEx: Tissue-level RNA expression across diverse samples
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- CellxGene: Single-cell RNA-seq data for specific cell types
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Target tissues/cell types:
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- Retina, photoreceptor cells (retinal rod, retinal cone)
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- Inner ear, hair cells (cochlea, vestibular)
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- Cilia-rich tissues (cerebellum, testis, fallopian tube)
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Expression enrichment in these tissues is evidence for potential cilia/Usher involvement.
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"""
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from usher_pipeline.evidence.expression.fetch import (
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fetch_hpa_expression,
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fetch_gtex_expression,
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fetch_cellxgene_expression,
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)
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from usher_pipeline.evidence.expression.transform import (
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calculate_tau_specificity,
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compute_expression_score,
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process_expression_evidence,
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)
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from usher_pipeline.evidence.expression.load import (
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load_to_duckdb,
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query_tissue_enriched,
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)
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from usher_pipeline.evidence.expression.models import (
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ExpressionRecord,
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EXPRESSION_TABLE_NAME,
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TARGET_TISSUES,
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)
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__all__ = [
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"fetch_hpa_expression",
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"fetch_gtex_expression",
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"fetch_cellxgene_expression",
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"calculate_tau_specificity",
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"compute_expression_score",
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"process_expression_evidence",
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"load_to_duckdb",
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"query_tissue_enriched",
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"ExpressionRecord",
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"EXPRESSION_TABLE_NAME",
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"TARGET_TISSUES",
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]
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