跳到主要内容
知仓学习社ZHICANG

bioc-pmc-api

Access PMC Open Access articles in BioC format for text mining

执行命令联网无严重或高危命中brycewang-stanford/Auto-Empirical-Research-Skills

它会碰到什么

扫了多少1 个文本文件,5 KB
它会碰到什么执行命令联网
命中总数5 处
命中统计严重 0 · 高 0 · 中 0 · 低 0

这一栏是扫描器报的事实,不是结论。命中多不等于有毒(安全工具、规则库、示例脚本本来就会包含危险写法),命中少也不等于干净。它和你手上的凭据、文件、网络有什么关系,需要你自己看。

技能内容

BioC API for PMC Open Access

Overview

The BioC API provides full-text articles from PubMed Central (PMC) in the BioC format — a simplified XML/JSON structure designed specifically for biomedical text mining. Unlike the standard PMC OAI service (which returns JATS XML), BioC pre-segments text into passages with offset annotations, making it ideal for NLP pipelines, named entity recognition, relation extraction, and other text mining tasks. Free, no authentication required.

API Endpoints

Base URL

https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/{PMCID}/unicode

Retrieve by PMC ID

# JSON format (recommended for programmatic use)
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/PMC6267067/unicode"

# XML format
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_xml/PMC6267067/unicode"

# ASCII encoding (strips non-ASCII characters)
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/PMC6267067/ascii"

Retrieve by PubMed ID

# Convert PMID to PMCID first, then query
curl "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=29346600&format=json"
# Returns: {"records": [{"pmid": "29346600", "pmcid": "PMC6267067", ...}]}

BioC JSON Structure

{
  "source": "PMC",
  "date": "2024-01-15",
  "key": "collection.key",
  "documents": [
    {
      "id": "PMC6267067",
      "passages": [
        {
          "infons": {
            "section_type": "TITLE",
            "type": "title"
          },
          "offset": 0,
          "text": "Article Title Here"
        },
        {
          "infons": {
            "section_type": "ABSTRACT",
            "type": "abstract"
          },
          "offset": 25,
          "text": "Background: This study investigates..."
        },
        {
          "infons": {
            "section_type": "INTRO",
            "type": "paragraph"
          },
          "offset": 350,
          "text": "The introduction text..."
        }
      ]
    }
  ]
}

Key fields:

  • passages[].infons.section_type: TITLE, ABSTRACT, INTRO, METHODS, RESULTS, DISCUSS, CONCL, REF, FIG, TABLE
  • passages[].offset: Character offset from document start
  • passages[].text: Plain text content of the passage

Python Usage

import requests
import json

def get_bioc_article(pmcid: str, fmt: str = "json") -> dict:
    """Fetch a PMC article in BioC format."""
    url = f"https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_{fmt}/{pmcid}/unicode"
    resp = requests.get(url, timeout=30)
    resp.raise_for_status()
    return resp.json() if fmt == "json" else resp.text

def extract_sections(bioc_doc: dict) -> dict:
    """Extract text organized by section type."""
    sections = {}
    for doc in bioc_doc.get("documents", []):
        for passage in doc.get("passages", []):
            section = passage.get("infons", {}).get("section_type", "OTHER")
            text = passage.get("text", "")
            sections.setdefault(section, []).append(text)
    return {k: "\n".join(v) for k, v in sections.items()}

# Example: fetch and parse
article = get_bioc_article("PMC6267067")
sections = extract_sections(article)
print(f"Title: {sections.get('TITLE', 'N/A')}")
print(f"Abstract length: {len(sections.get('ABSTRACT', ''))} chars")
print(f"Sections found: {list(sections.keys())}")

Data Coverage

  • PMC Open Access Subset: ~4M+ articles with CC licenses
  • Author Manuscript Collection: NIH-funded author manuscripts
  • Updates: New articles added daily

Rate Limits

  • Follow NCBI standard: 3 requests per second
  • For bulk access, use the PMC FTP service instead
  • Add tool=your_tool_name&email=your@email.com to requests for priority queue

Citation

When using this API in publications, cite:

> Comeau DC, Wei CH, Islamaj Dogan R, Lu Z. PMC text mining subset in BioC: about 3 million full text articles and growing. Bioinformatics, btz070, 2019.

References

想直接用这个技能?

本站把开放许可(MIT / Apache 等)的技能按仓库打包整理到网盘,点一下转存到你自己的网盘,不用一个个从 GitHub 拉。许可未声明的技能只给原始仓库链接,不打包。