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0ccfe4a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | from __future__ import annotations
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ALSPaper:
pmid: str
title: str
abstract: str
authors: list[str]
year: int
doi: str
mesh_terms: list[str]
# Populated after PMC fetch (None if not Open Access)
full_text: Optional[str] = None
# Populated after Semantic Scholar enrichment
citation_count: int = 0
# Populated after entity extraction
entity_names: list[str] = field(default_factory=list)
def to_dict(self) -> dict:
return {
"pmid": self.pmid,
"title": self.title,
"abstract": self.abstract,
"authors": self.authors,
"year": self.year,
"doi": self.doi,
"mesh_terms": self.mesh_terms,
"full_text": self.full_text,
"citation_count": self.citation_count,
"entity_names": self.entity_names,
}
@classmethod
def from_dict(cls, d: dict) -> ALSPaper:
return cls(
pmid=d["pmid"],
title=d["title"],
abstract=d["abstract"],
authors=d.get("authors", []),
year=d.get("year", 0),
doi=d.get("doi", ""),
mesh_terms=d.get("mesh_terms", []),
full_text=d.get("full_text"),
citation_count=d.get("citation_count", 0),
entity_names=d.get("entity_names", []),
)
@dataclass
class ExtractedEntity:
type: str # Gene | Protein | Compound | Pathway | Phenotype | Mechanism
name: str # raw name from text
canonical_id: str # normalized canonical identifier
confidence: float # 0.0–1.0
mentions: int # occurrence count in paper
@dataclass
class EntityRelationship:
source: str # canonical_id of source entity
target: str # canonical_id of target entity
relation_type: str # BINDS | INHIBITS | ASSOCIATED_WITH | TESTED_IN | EXPRESSED_IN | CO_OCCURS
evidence_pmids: list[str]
confidence: float # average confidence across supporting papers
evidence_text: str = "" # representative excerpt from the paper
@dataclass
class PaperExtractionResult:
pmid: str
entities: list[ExtractedEntity]
relationships: list[EntityRelationship]
def to_dict(self) -> dict:
return {
"pmid": self.pmid,
"entities": [
{
"type": e.type,
"name": e.name,
"canonical_id": e.canonical_id,
"confidence": e.confidence,
"mentions": e.mentions,
}
for e in self.entities
],
"relationships": [
{
"source": r.source,
"target": r.target,
"relation_type": r.relation_type,
"evidence_pmids": r.evidence_pmids,
"confidence": r.confidence,
"evidence_text": r.evidence_text,
}
for r in self.relationships
],
}
@dataclass
class TrialSummary:
nct_id: str
title: str
phase: str
status: str
interventions: list[str]
target_entities: list[str] # canonical_ids of targeted genes/proteins/compounds
sponsor: str = ""
start_date: str = ""
url: str = ""
@dataclass
class ResearchLandscape:
query: str
mechanisms: list[str] # 2–3 key mechanism bullet points
entities: list[dict] # {name, type, description, paper_count}
papers: list[dict] # top 5: {pmid, title, year, doi, citation_count}
trials: list[TrialSummary]
evidence_count: int # total papers retrieved
generated_at: str # ISO timestamp
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