Medgeclaw GuideSAFE

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---
name: medgeclaw-guide
description: "AI research assistant for biomedicine, RNA-seq, and drug discovery"
metadata:
  openclaw:
    emoji: "💊"
    category: "domains"
    subcategory: "biomedical"
    keywords: ["biomedicine", "RNA-seq", "drug discovery", "clinical AI", "medical NLP", "bioinformatics"]
    source: "wentor-research-plugins"
---

# MedgeClaw Guide

## Overview

MedgeClaw is a conceptual framework for AI-powered biomedical research assistance, integrating natural language processing for medical literature, computational biology pipelines, and drug discovery workflows. The name reflects the integration of Medical knowledge Edge (cutting-edge biomedical AI) with the Claw agent pattern for autonomous research execution.

Biomedical research is uniquely suited for AI augmentation because it generates massive, heterogeneous data -- genomic sequences, clinical records, imaging data, molecular structures, and published literature -- that exceeds the capacity of individual researchers to synthesize. AI systems that can navigate across these data types, identify patterns, and suggest hypotheses accelerate the pace of discovery.

This guide covers the key computational methods in biomedical AI research: medical NLP for literature mining, RNA-seq analysis pipelines, drug discovery computational workflows, and the integration patterns that connect these components into coherent research workflows. The focus is on methods that are reproducible, validated, and suitable for publication in biomedical journals.

## Medical NLP and Literature Mining

### Biomedical Named Entity Recognition

```python
# Biomedical NER using scispaCy
import scispacy
import spacy
from scispacy.linking import EntityLinker

# Load biomedical NER model
nlp = spacy.load("en_ner_bionlp13cg_md")

# Add UMLS entity linker for concept normalization
nlp.add_pipe("scispacy_linker", config={
    "resolve_abbreviations": True,
    "linker_name": "umls",
})

def extract_biomedical_entities(text: str) -> dict:
    """
    Extract and normalize biomedical entities from text.
    Returns genes, chemicals, diseases, and their UMLS mappings.
    """
    doc = nlp(text)
    entities = {
        "genes": [],
        "chemicals": [],
        "diseases": [],
        "other": [],
    }

    category_map = {
        "GENE_OR_GENE_PRODUCT": "genes",
        "SIMPLE_CHEMICAL": "chemicals",
        "CANCER": "diseases",
        "ORGAN": "other",
        "CELL": "other",
    }

    for ent in doc.ents:
        category = category_map.get(ent.label_, "other")
        entity_info = {
            "text": ent.text,
            "label": ent.label_,
            "start": ent.start_char,
            "end": ent.end_char,
        }

        # Add UMLS links if available
        if hasattr(ent, "_") and hasattr(ent._, "kb_ents"):
            if ent._.kb_ents:
                top_link = ent._.kb_ents[0]
                entity_info["umls_cui"] = top_link[0]
                entity_info["confidence"] = round(top_link[1], 3)

        entities[category].append(entity_info)

    return entities
```

### Systematic Literature Search Pipeline

```python
from Bio import Entrez
import time

Entrez.email = "[email protected]"

def systematic_pubmed_search(
    query: str,
    max_results: int = 1000,
    date_range: tuple = ("2020/01/01", "2025/12/31"),
) -> list:
    """
    Conduct a systematic PubMed search with structured result extraction.
    Suitable for systematic reviews and meta-analyses.
    """
    # Step 1: Search PubMed
    handle = Entrez.esearch(
        db="pubmed",
        term=query,
        retmax=max_results,
        datetype="pdat",
        mindate=date_range[0],
        maxdate=date_range[1],
        sort="relevance",
    )
    results = Entrez.read(handle)
    handle.close()

    pmids = results["IdList"]
    print(f"Found {results['Count']} results, retrieving {len(pmids)}")

    # Step 2: Fetch article details in batches
    articles = []
    batch_size = 100
    for i in range(0, len(pmids), batch_size):
        batch = pmids[i:i + batch_size]
        handle = Entrez.efetch(
            db="pubmed", id=",".join(batch),
            rettype="xml", retmode="xml"
        )
        records = Entrez.read(handle)
        handle.close()

        for article in records["PubmedArticle"]:
            medline = article["MedlineCitation"]
            art = medline["Article"]
            articles.append({
                "pmid": str(medline["PMID"]),
                "title": art["ArticleTitle"],
                "abstract": art.get("Abstract", {}).get("AbstractText", [""])[0],
                "journal": art["Journal"]["Title"],
                "year": art["Journal"]["JournalIssue"]["PubDate"].get("Year", "N/A"),
                "mesh_terms": [
                    d["DescriptorName"]
                    for d in medline.get("MeshHeadingList", [])
                ] if "MeshHeadingList" in medline else [],
            })

        time.sleep(0.4)  # Respect NCBI rate limits

    return articles
```

## RNA-seq Analysis

### Complete DESeq2 Workflow

```r
# Complete RNA-seq differential expression analysis with DESeq2
# This is the standard workflow for biomedical RNA-seq papers

library(DESeq2)
library(ggplot2)
library(EnhancedVolcano)
library(clusterProfiler)
library(org.Hs.eg.db)

# --- 1. Load count matrix and metadata ---
counts <- read.csv("raw_counts.csv", row.names = 1)
coldata <- read.csv("sample_info.csv", row.names = 1)

# Verify sample order matches
stopifnot(all(colnames(counts) == rownames(coldata)))

# --- 2. Create DESeq2 object ---
dds <- DESeqDataSetFromMatrix(
  countData = counts,
  colData = coldata,
  design = ~ condition  # Simple two-group comparison
)

# Pre-filtering: remove low-count genes
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep, ]

# --- 3. Run differential expression ---
dds <- DESeq(dds)
res <- results(dds, contrast = c("condition", "treatment", "control"),
               alpha = 0.05)

# Summary
summary

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