---
title: BioBERT quickstart
description: BioBERT is deprecated and chat cannot call it. For reference, the six-step path from biomedical text to annotated entities, relation triples or answers.
---

You want structured entities and relationships out of biomedical text; this runs your first job in six steps — pick a task (NER, relation extraction, or question answering), enter or upload your text, and export the annotated results.

> **Not available in chat**

> BioBERT is deprecated on the platform and not registered with the orchestrator, so chat cannot call it. A current LLM performs BioBERT's entity extraction and relevance ranking natively over text retrieved with [PubMed](/docs/pubmed); for curated identifiers an LLM must not invent (NCBI Gene IDs, MeSH terms), use PubTator3. Existing BioBERT job results remain viewable.

## Quick biomedical text mining with BioBERT

## Step-by-step workflow

### 1. Access the BioBERT tool

BioBERT no longer has a form in .bloom*'s Tools panel, and chat cannot call it (see the note above). The steps below describe the BioBERT workflow so you can read existing BioBERT results. BioBERT provides named entity recognition, relation extraction, and question answering for biomedical text.

### 2. Choose your task

**Named Entity Recognition (NER):**
- Identify genes, diseases, drugs, and other entities
- Get entity spans and classifications
- Batch process multiple documents

**Relation Extraction (RE):**
- Extract relationships between entities
- Drug-disease, protein-protein, gene-disease links
- Evidence sentences for each relationship

**Question Answering (QA):**
- Ask questions about biomedical text
- Get extracted answers with evidence
- Support for complex biomedical queries

### 3. Enter your text

**Direct text input:**
```
Aspirin inhibits cyclooxygenase (COX) enzymes, reducing
prostaglandin synthesis. This mechanism provides anti-inflammatory
and analgesic effects. Common side effects include gastrointestinal
bleeding and increased bleeding time.
```

**File upload:**
- Plain text files (.txt)
- PDF documents (text extraction)
- CSV with text column

### 4. Configure settings

**Entity types (NER):**
- [ ] Genes/Proteins
- [ ] Diseases
- [ ] Chemicals/Drugs
- [ ] Species
- [ ] Mutations

**Relationship types (RE):**
- [ ] Drug-Disease
- [ ] Drug-Target
- [ ] Gene-Disease
- [ ] Drug-Drug Interaction

### 5. View results

**NER output example:**
```
[Aspirin]DRUG inhibits [cyclooxygenase]PROTEIN ([COX]PROTEIN)
enzymes, reducing [prostaglandin]CHEMICAL synthesis.
Side effects include [gastrointestinal bleeding]DISEASE.
```

**RE output example:**
```
Relation: DRUG-TARGET
Entity 1: Aspirin (Drug)
Entity 2: cyclooxygenase (Protein)
Relation type: inhibits
Confidence: 0.94
```

### 6. Export and integrate

**Export formats:**
- Annotated text (standoff format)
- CSV with entities and relationships
- JSON for programmatic access

**Integration:**
- Feed entities to knowledge graphs
- Use relationships for target discovery
- Support literature review workflows

## Common use cases

### Literature screening
1. Upload batch of abstracts
2. Run NER to identify key entities
3. Filter by entity types of interest
4. Export prioritized document list

### Target identification
1. Input disease-focused literature
2. Extract gene-disease relationships
3. Rank potential targets by evidence
4. Export for validation studies

### Competitive intelligence
1. Monitor competitor publications
2. Extract drug-target-disease triples
3. Track development pipelines
4. Generate landscape reports

## Read next