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+# Biomni Use Cases and Examples
+
+Comprehensive examples demonstrating biomni across biomedical research domains.
+
+## Table of Contents
+
+1. [CRISPR Screening and Gene Editing](#crispr-screening-and-gene-editing)
+2. [Single-Cell RNA-seq Analysis](#single-cell-rna-seq-analysis)
+3. [Drug Discovery and ADMET](#drug-discovery-and-admet)
+4. [GWAS and Genetic Analysis](#gwas-and-genetic-analysis)
+5. [Clinical Genomics and Diagnostics](#clinical-genomics-and-diagnostics)
+6. [Protein Structure and Function](#protein-structure-and-function)
+7. [Literature and Knowledge Synthesis](#literature-and-knowledge-synthesis)
+8. [Multi-Omics Integration](#multi-omics-integration)
+
+---
+
+## CRISPR Screening and Gene Editing
+
+### Example 1: Genome-Wide CRISPR Screen Design
+
+**Task:** Design a CRISPR knockout screen to identify genes regulating autophagy.
+
+```python
+from biomni.agent import A1
+
+agent = A1(path='./data', llm='claude-sonnet-4-20250514')
+
+result = agent.go("""
+Design a genome-wide CRISPR knockout screen to identify genes regulating
+autophagy in HEK293 cells.
+
+Requirements:
+1. Generate comprehensive sgRNA library targeting all protein-coding genes
+2. Design 4 sgRNAs per gene with optimal on-target and minimal off-target scores
+3. Include positive controls (known autophagy regulators: ATG5, BECN1, ULK1)
+4. Include negative controls (non-targeting sgRNAs)
+5. Prioritize genes based on:
+   - Existing autophagy pathway annotations
+   - Protein-protein interactions with known autophagy factors
+   - Expression levels in HEK293 cells
+6. Output sgRNA sequences, scores, and gene prioritization rankings
+
+Provide analysis as Python code and interpret results.
+""")
+
+agent.save_conversation_history("autophagy_screen_design.pdf")
+```
+
+**Expected Output:**
+- sgRNA library with ~80,000 guides (4 per gene × ~20,000 genes)
+- On-target and off-target scores for each sgRNA
+- Prioritized gene list based on pathway enrichment
+- Quality control metrics for library design
+
+### Example 2: CRISPR Off-Target Prediction
+
+```python
+result = agent.go("""
+Analyze potential off-target effects for this sgRNA sequence:
+GCTGAAGATCCAGTTCGATG
+
+Tasks:
+1. Identify all genomic locations with ≤3 mismatches
+2. Score each potential off-target site
+3. Assess likelihood of cleavage at off-target sites
+4. Recommend whether sgRNA is suitable for use
+5. If unsuitable, suggest alternative sgRNAs for the same gene
+""")
+```
+
+### Example 3: Screen Hit Analysis
+
+```python
+result = agent.go("""
+Analyze CRISPR screen results from autophagy phenotype screen.
+
+Input file: screen_results.csv
+Columns: sgRNA_ID, gene, log2_fold_change, p_value, FDR
+
+Tasks:
+1. Identify significant hits (FDR < 0.05, |LFC| > 1.5)
+2. Perform gene ontology enrichment on hit genes
+3. Map hits to known autophagy pathways
+4. Identify novel candidates not previously linked to autophagy
+5. Predict functional relationships between hit genes
+6. Generate visualization of hit genes in pathway context
+""")
+```
+
+---
+
+## Single-Cell RNA-seq Analysis
+
+### Example 1: Cell Type Annotation
+
+**Task:** Analyze single-cell RNA-seq data and annotate cell populations.
+
+```python
+agent = A1(path='./data', llm='claude-sonnet-4-20250514')
+
+result = agent.go("""
+Analyze single-cell RNA-seq dataset from human PBMC sample.
+
+File: pbmc_data.h5ad (10X Genomics format)
+
+Workflow:
+1. Quality control:
+   - Filter cells with <200 or >5000 detected genes
+   - Remove cells with >20% mitochondrial content
+   - Filter genes detected in <3 cells
+
+2. Normalization and preprocessing:
+   - Normalize to 10,000 reads per cell
+   - Log-transform
+   - Identify highly variable genes
+   - Scale data
+
+3. Dimensionality reduction:
+   - PCA (50 components)
+   - UMAP visualization
+
+4. Clustering:
+   - Leiden algorithm with resolution=0.8
+   - Identify cluster markers (Wilcoxon rank-sum test)
+
+5. Cell type annotation:
+   - Annotate clusters using marker genes:
+     * T cells (CD3D, CD3E)
+     * B cells (CD79A, MS4A1)
+     * NK cells (GNLY, NKG7)
+     * Monocytes (CD14, LYZ)
+     * Dendritic cells (FCER1A, CST3)
+
+6. Generate UMAP plots with annotations and export results
+""")
+
+agent.save_conversation_history("pbmc_scrna_analysis.pdf")
+```
+
+### Example 2: Differential Expression Analysis
+
+```python
+result = agent.go("""
+Perform differential expression analysis between conditions in scRNA-seq data.
+
+Data: pbmc_treated_vs_control.h5ad
+Conditions: treated (drug X) vs control
+
+Tasks:
+1. Identify differentially expressed genes for each cell type
+2. Use statistical tests appropriate for scRNA-seq (MAST or Wilcoxon)
+3. Apply multiple testing correction (Benjamini-Hochberg)
+4. Threshold: |log2FC| > 0.5, adjusted p < 0.05
+5. Perform pathway enrichment on DE genes per cell type
+6. Identify cell-type-specific drug responses
+7. Generate volcano plots and heatmaps
+""")
+```
+
+### Example 3: Trajectory Analysis
+
+```python
+result = agent.go("""
+Perform pseudotime trajectory analysis on differentiation dataset.
+
+Data: hematopoiesis_scrna.h5ad
+Starting population: Hematopoietic stem cells (HSCs)
+
+Analysis:
+1. Subset to hematopoietic lineages
+2. Compute diffusion map or PAGA for trajectory inference
+3. Order cells along pseudotime
+4. Identify genes with dynamic expression along trajectory
+5. Cluster genes by expression patterns
+6. Map trajectories to known differentiation pathways
+7. Visualize key transcription factors driving differentiation
+""")
+```
+
+---
+
+## Drug Discovery and ADMET
+
+### Example 1: ADMET Property Prediction
+
+**Task:** Predict ADMET properties for drug candidates.
+
+```python
+agent = A1(path='./data', llm='claude-sonnet-4-20250514')
+
+result = agent.go("""
+Predict ADMET properties for these drug candidates:
+
+Compounds (SMILES format):
+1. CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5
+2. CN1CCN(CC1)C2=C(C=C3C(=C2)N=CN=C3NC4=CC=C(C=C4)F)OC
+3. CC(C)(C)NC(=O)N(CC1=CC=CC=C1)C2CCN(CC2)C(=O)C3=CC4=C(C=C3)OCO4
+
+For each compound, predict:
+
+**Absorption:**
+- Caco-2 permeability (cm/s)
+- Human intestinal absorption (HIA %)
+- Oral bioavailability
+
+**Distribution:**
+- Plasma protein binding (%)
+- Blood-brain barrier penetration (BBB+/-)
+- Volume of distribution (L/kg)
+
+**Metabolism:**
+- CYP450 substrate/inhibitor predictions (2D6, 3A4, 2C9, 2C19)
+- Metabolic stability (T1/2)
+
+**Excretion:**
+- Clearance (mL/min/kg)
+- Half-life (hours)
+
+**Toxicity:**
+- hERG IC50 (cardiotoxicity risk)
+- Hepatotoxicity prediction
+- Ames mutagenicity
+- LD50 estimates
+
+Provide predictions with confidence scores and flag any red flags.
+""")
+
+agent.save_conversation_history("admet_predictions.pdf")
+```
+
+### Example 2: Target Identification
+
+```python
+result = agent.go("""
+Identify potential protein targets for Alzheimer's disease drug development.
+
+Tasks:
+1. Query GWAS data for Alzheimer's-associated genes
+2. Identify genes with druggable domains (kinases, GPCRs, ion channels, etc.)
+3. Check for brain expression patterns
+4. Assess disease relevance via literature mining
+5. Evaluate existing chemical probe availability
+6. Rank targets by:
+   - Genetic evidence strength
+   - Druggability
+   - Lack of existing therapies
+7. Suggest target validation experiments
+""")
+```
+
+### Example 3: Virtual Screening
+
+```python
+result = agent.go("""
+Perform virtual screening for EGFR kinase inhibitors.
+
+Database: ZINC15 lead-like subset (~6M compounds)
+Target: EGFR kinase domain (PDB: 1M17)
+
+Workflow:
+1. Prepare protein structure (remove waters, add hydrogens)
+2. Define binding pocket (based on erlotinib binding site)
+3. Generate pharmacophore model from known EGFR inhibitors
+4. Filter ZINC database by:
+   - Molecular weight: 200-500 Da
+   - LogP: 0-5
+   - Lipinski's rule of five
+   - Pharmacophore match
+5. Dock top 10,000 compounds
+6. Score by docking energy and predicted binding affinity
+7. Select top 100 for further analysis
+8. Predict ADMET properties for top hits
+9. Recommend top 10 compounds for experimental validation
+""")
+```
+
+---
+
+## GWAS and Genetic Analysis
+
+### Example 1: GWAS Summary Statistics Analysis
+
+**Task:** Interpret GWAS results and identify causal genes.
+
+```python
+agent = A1(path='./data', llm='claude-sonnet-4-20250514')
+
+result = agent.go("""
+Analyze GWAS summary statistics for Type 2 Diabetes.
+
+Input file: t2d_gwas_summary.txt
+Columns: CHR, BP, SNP, P, OR, BETA, SE, A1, A2
+
+Analysis steps:
+1. Identify genome-wide significant variants (P < 5e-8)
+2. Perform LD clumping to identify independent signals
+3. Map variants to genes using:
+   - Nearest gene
+   - eQTL databases (GTEx)
+   - Hi-C chromatin interactions
+4. Prioritize causal genes using multiple evidence:
+   - Fine-mapping scores
+   - Coding variant consequences
+   - Gene expression in relevant tissues (pancreas, liver, adipose)
+   - Pathway enrichment
+5. Identify druggable targets among causal genes
+6. Compare with known T2D genes and highlight novel associations
+7. Generate Manhattan plot, QQ plot, and gene prioritization table
+""")
+
+agent.save_conversation_history("t2d_gwas_analysis.pdf")
+```
+
+### Example 2: Polygenic Risk Score
+
+```python
+result = agent.go("""
+Develop and validate polygenic risk score (PRS) for coronary artery disease (CAD).
+
+Training GWAS: CAD_discovery_summary_stats.txt (N=180,000)
+Validation cohort: CAD_validation_genotypes.vcf (N=50,000)
+
+Tasks:
+1. Select variants for PRS using p-value thresholding (P < 1e-5)
+2. Perform LD clumping (r² < 0.1, 500kb window)
+3. Calculate PRS weights from GWAS betas
+4. Compute PRS for validation cohort individuals
+5. Evaluate PRS performance:
+   - AUC for CAD case/control discrimination
+   - Odds ratios across PRS deciles
+   - Compare to traditional risk factors (age, sex, BMI, smoking)
+6. Assess PRS calibration and create risk stratification plot
+7. Identify high-risk individuals (top 5% PRS)
+""")
+```
+
+### Example 3: Variant Pathogenicity Prediction
+
+```python
+result = agent.go("""
+Predict pathogenicity of rare coding variants in candidate disease genes.
+
+Variants (VCF format):
+- chr17:41234451:A>G (BRCA1 p.Arg1347Gly)
+- chr2:179428448:C>T (TTN p.Trp13579*)
+- chr7:117188679:G>A (CFTR p.Gly542Ser)
+
+For each variant, assess:
+1. In silico predictions (SIFT, PolyPhen2, CADD, REVEL)
+2. Population frequency (gnomAD)
+3. Evolutionary conservation (PhyloP, PhastCons)
+4. Protein structure impact (using AlphaFold structures)
+5. Functional domain location
+6. ClinVar annotations (if available)
+7. Literature evidence
+8. ACMG/AMP classification criteria
+
+Provide pathogenicity classification (benign, likely benign, VUS, likely pathogenic, pathogenic) with supporting evidence.
+""")
+```
+
+---
+
+## Clinical Genomics and Diagnostics
+
+### Example 1: Rare Disease Diagnosis
+
+**Task:** Diagnose rare genetic disease from whole exome sequencing.
+
+```python
+agent = A1(path='./data', llm='claude-sonnet-4-20250514')
+
+result = agent.go("""
+Analyze whole exome sequencing (WES) data for rare disease diagnosis.
+
+Patient phenotypes (HPO terms):
+- HP:0001250 (Seizures)
+- HP:0001249 (Intellectual disability)
+- HP:0001263 (Global developmental delay)
+- HP:0001252 (Hypotonia)
+
+VCF file: patient_trio.vcf (proband + parents)
+
+Analysis workflow:
+1. Variant filtering:
+   - Quality filters (QUAL > 30, DP > 10, GQ > 20)
+   - Frequency filters (gnomAD AF < 0.01)
+   - Functional impact (missense, nonsense, frameshift, splice site)
+
+2. Inheritance pattern analysis:
+   - De novo variants
+   - Autosomal recessive (compound het, homozygous)
+   - X-linked
+
+3. Phenotype-driven prioritization:
+   - Match candidate genes to HPO terms
+   - Use HPO-gene associations
+   - Check gene expression in relevant tissues (brain)
+
+4. Variant pathogenicity assessment:
+   - In silico predictions
+   - ACMG classification
+   - Literature evidence
+
+5. Generate diagnostic report with:
+   - Top candidate variants
+   - Supporting evidence
+   - Functional validation suggestions
+   - Genetic counseling recommendations
+""")
+
+agent.save_conversation_history("rare_disease_diagnosis.pdf")
+```
+
+### Example 2: Cancer Genomics Analysis
+
+```python
+result = agent.go("""
+Analyze tumor-normal paired sequencing for cancer genomics.
+
+Files:
+- tumor_sample.vcf (somatic variants)
+- tumor_rnaseq.bam (gene expression)
+- tumor_cnv.seg (copy number variants)
+
+Analysis:
+1. Identify driver mutations:
+   - Known cancer genes (COSMIC, OncoKB)
+   - Recurrent hotspot mutations
+   - Truncating mutations in tumor suppressors
+
+2. Analyze mutational signatures:
+   - Decompose signatures (COSMIC signatures)
+   - Identify mutagenic processes
+
+3. Copy number analysis:
+   - Identify amplifications and deletions
+   - Focal vs. arm-level events
+   - Assess oncogene amplifications and TSG deletions
+
+4. Gene expression analysis:
+   - Identify outlier gene expression
+   - Fusion transcript detection
+   - Pathway dysregulation
+
+5. Therapeutic implications:
+   - Match alterations to FDA-approved therapies
+   - Identify clinical trial opportunities
+   - Predict response to targeted therapies
+
+6. Generate precision oncology report
+""")
+```
+
+### Example 3: Pharmacogenomics
+
+```python
+result = agent.go("""
+Generate pharmacogenomics report for patient genotype data.
+
+VCF file: patient_pgx.vcf
+
+Analyze variants affecting drug metabolism:
+
+**CYP450 genes:**
+- CYP2D6 (affects ~25% of drugs)
+- CYP2C19 (clopidogrel, PPIs, antidepressants)
+- CYP2C9 (warfarin, NSAIDs)
+- CYP3A5 (tacrolimus, immunosuppressants)
+
+**Drug transporter genes:**
+- SLCO1B1 (statin myopathy risk)
+- ABCB1 (P-glycoprotein)
+
+**Drug targets:**
+- VKORC1 (warfarin dosing)
+- DPYD (fluoropyrimidine toxicity)
+- TPMT (thiopurine toxicity)
+
+For each gene:
+1. Determine diplotype (*1/*1, *1/*2, etc.)
+2. Assign metabolizer phenotype (PM, IM, NM, RM, UM)
+3. Provide dosing recommendations using CPIC/PharmGKB guidelines
+4. Flag high-risk drug-gene interactions
+5. Suggest alternative medications if needed
+
+Generate patient-friendly report with actionable recommendations.
+""")
+```
+
+---
+
+## Protein Structure and Function
+
+### Example 1: AlphaFold Structure Analysis
+
+```python
+agent = A1(path='./data', llm='claude-sonnet-4-20250514')
+
+result = agent.go("""
+Analyze AlphaFold structure prediction for novel protein.
+
+Protein: Hypothetical protein ABC123 (UniProt: Q9XYZ1)
+
+Tasks:
+1. Retrieve AlphaFold structure from database
+2. Assess prediction quality:
+   - pLDDT scores per residue
+   - Identify high-confidence regions (pLDDT > 90)
+   - Flag low-confidence regions (pLDDT < 50)
+
+3. Structural analysis:
+   - Identify domains using structural alignment
+   - Predict fold family
+   - Identify secondary structure elements
+
+4. Functional prediction:
+   - Search for structural homologs in PDB
+   - Identify conserved functional sites
+   - Predict binding pockets
+   - Suggest possible ligands/substrates
+
+5. Variant impact analysis:
+   - Map disease-associated variants to structure
+   - Predict structural consequences
+   - Identify variants affecting binding sites
+
+6. Generate PyMOL visualization scripts highlighting key features
+""")
+
+agent.save_conversation_history("alphafold_analysis.pdf")
+```
+
+### Example 2: Protein-Protein Interaction Prediction
+
+```python
+result = agent.go("""
+Predict and analyze protein-protein interactions for autophagy pathway.
+
+Query proteins: ATG5, ATG12, ATG16L1
+
+Analysis:
+1. Retrieve known interactions from:
+   - STRING database
+   - BioGRID
+   - IntAct
+   - Literature mining
+
+2. Predict novel interactions using:
+   - Structural modeling (AlphaFold-Multimer)
+   - Coexpression analysis
+   - Phylogenetic profiling
+
+3. Analyze interaction interfaces:
+   - Identify binding residues
+   - Assess interface properties (area, hydrophobicity)
+   - Predict binding affinity
+
+4. Functional analysis:
+   - Map interactions to autophagy pathway steps
+   - Identify regulatory interactions
+   - Predict complex stoichiometry
+
+5. Therapeutic implications:
+   - Identify druggable interfaces
+   - Suggest peptide inhibitors
+   - Design disruption strategies
+
+Generate network visualization and interaction details.
+""")
+```
+
+---
+
+## Literature and Knowledge Synthesis
+
+### Example 1: Systematic Literature Review
+
+```python
+agent = A1(path='./data', llm='claude-sonnet-4-20250514')
+
+result = agent.go("""
+Perform systematic literature review on CRISPR base editing applications.
+
+Search query: "CRISPR base editing" OR "base editor" OR "CBE" OR "ABE"
+Date range: 2016-2025
+
+Tasks:
+1. Search PubMed and retrieve relevant abstracts
+2. Filter for original research articles
+3. Extract key information:
+   - Base editor type (CBE, ABE, dual)
+   - Target organism/cell type
+   - Application (disease model, therapy, crop improvement)
+   - Editing efficiency
+   - Off-target assessment
+
+4. Categorize applications:
+   - Therapeutic applications (by disease)
+   - Agricultural applications
+   - Basic research
+
+5. Analyze trends:
+   - Publications over time
+   - Most studied diseases
+   - Evolution of base editor technology
+
+6. Synthesize findings:
+   - Clinical trial status
+   - Remaining challenges
+   - Future directions
+
+Generate comprehensive review document with citation statistics.
+""")
+
+agent.save_conversation_history("crispr_base_editing_review.pdf")
+```
+
+### Example 2: Gene Function Synthesis
+
+```python
+result = agent.go("""
+Synthesize knowledge about gene function from multiple sources.
+
+Target gene: PARK7 (DJ-1)
+
+Integrate information from:
+1. **Genetic databases:**
+   - NCBI Gene
+   - UniProt
+   - OMIM
+
+2. **Expression data:**
+   - GTEx tissue expression
+   - Human Protein Atlas
+   - Single-cell expression atlases
+
+3. **Functional data:**
+   - GO annotations
+   - KEGG pathways
+   - Reactome
+   - Protein interactions (STRING)
+
+4. **Disease associations:**
+   - ClinVar variants
+   - GWAS catalog
+   - Disease databases (DisGeNET)
+
+5. **Literature:**
+   - PubMed abstracts
+   - Key mechanistic studies
+   - Review articles
+
+Synthesize into comprehensive gene report:
+- Molecular function
+- Biological processes
+- Cellular localization
+- Tissue distribution
+- Disease associations
+- Known drug targets/inhibitors
+- Unresolved questions
+
+Generate structured summary suitable for research planning.
+""")
+```
+
+---
+
+## Multi-Omics Integration
+
+### Example 1: Multi-Omics Disease Analysis
+
+```python
+agent = A1(path='./data', llm='claude-sonnet-4-20250514')
+
+result = agent.go("""
+Integrate multi-omics data to understand disease mechanism.
+
+Disease: Alzheimer's disease
+Data types:
+- Genomics: GWAS summary statistics (gwas_ad.txt)
+- Transcriptomics: Brain RNA-seq (controls vs AD, rnaseq_data.csv)
+- Proteomics: CSF proteomics (proteomics_csf.csv)
+- Metabolomics: Plasma metabolomics (metabolomics_plasma.csv)
+- Epigenomics: Brain methylation array (methylation_data.csv)
+
+Integration workflow:
+1. Analyze each omics layer independently:
+   - Identify significantly altered features
+   - Perform pathway enrichment
+
+2. Cross-omics correlation:
+   - Correlate gene expression with protein levels
+   - Link genetic variants to expression (eQTL)
+   - Associate methylation with gene expression
+   - Connect proteins to metabolites
+
+3. Network analysis:
+   - Build multi-omics network
+   - Identify key hub genes/proteins
+   - Detect disease modules
+
+4. Causal inference:
+   - Prioritize drivers vs. consequences
+   - Identify therapeutic targets
+   - Predict drug mechanisms
+
+5. Generate integrative model of AD pathogenesis
+
+Provide visualization and therapeutic target recommendations.
+""")
+
+agent.save_conversation_history("ad_multiomics_analysis.pdf")
+```
+
+### Example 2: Systems Biology Modeling
+
+```python
+result = agent.go("""
+Build systems biology model of metabolic pathway.
+
+Pathway: Glycolysis
+Data sources:
+- Enzyme kinetics (BRENDA database)
+- Metabolite concentrations (literature)
+- Gene expression (tissue-specific, GTEx)
+- Flux measurements (C13 labeling studies)
+
+Modeling tasks:
+1. Construct pathway model:
+   - Define reactions and stoichiometry
+   - Parameterize enzyme kinetics (Km, Vmax, Ki)
+   - Set initial metabolite concentrations
+
+2. Simulate pathway dynamics:
+   - Steady-state analysis
+   - Time-course simulations
+   - Sensitivity analysis
+
+3. Constraint-based modeling:
+   - Flux balance analysis (FBA)
+   - Identify bottleneck reactions
+   - Predict metabolic engineering strategies
+
+4. Integrate with gene expression:
+   - Tissue-specific model predictions
+   - Disease vs. normal comparisons
+
+5. Therapeutic predictions:
+   - Enzyme inhibition effects
+   - Metabolic rescue strategies
+   - Drug target identification
+
+Generate model in SBML format and simulation results.
+""")
+```
+
+---
+
+## Best Practices for Task Formulation
+
+### 1. Be Specific and Detailed
+
+**Poor:**
+```python
+agent.go("Analyze this RNA-seq data")
+```
+
+**Good:**
+```python
+agent.go("""
+Analyze bulk RNA-seq data from cancer vs. normal samples.
+
+Files: cancer_rnaseq.csv (TPM values, 50 cancer, 50 normal)
+
+Tasks:
+1. Differential expression (DESeq2, padj < 0.05, |log2FC| > 1)
+2. Pathway enrichment (KEGG, Reactome)
+3. Generate volcano plot and top DE gene heatmap
+""")
+```
+
+### 2. Include File Paths and Formats
+
+Always specify:
+- Exact file paths
+- File formats (VCF, BAM, CSV, H5AD, etc.)
+- Data structure (columns, sample IDs)
+
+### 3. Set Clear Success Criteria
+
+Define thresholds and cutoffs:
+- Statistical significance (P < 0.05, FDR < 0.1)
+- Fold change thresholds
+- Quality filters
+- Expected outputs
+
+### 4. Request Visualizations
+
+Explicitly ask for plots:
+- Volcano plots, MA plots
+- Heatmaps, PCA plots
+- Network diagrams
+- Manhattan plots
+
+### 5. Specify Biological Context
+
+Include:
+- Organism (human, mouse, etc.)
+- Tissue/cell type
+- Disease/condition
+- Treatment details
+
+### 6. Request Interpretations
+
+Ask agent to:
+- Interpret biological significance
+- Suggest follow-up experiments
+- Identify limitations
+- Provide literature context
+
+---
+
+## Common Patterns
+
+### Data Quality Control
+
+```python
+"""
+Before analysis, perform quality control:
+1. Check for missing values
+2. Assess data distributions
+3. Identify outliers
+4. Generate QC report
+Only proceed with analysis if data passes QC.
+"""
+```
+
+### Iterative Refinement
+
+```python
+"""
+Perform analysis in stages:
+1. Initial exploratory analysis
+2. Based on results, refine parameters
+3. Focus on interesting findings
+4. Generate final report
+
+Show intermediate results for each stage.
+"""
+```
+
+### Reproducibility
+
+```python
+"""
+Ensure reproducibility:
+1. Set random seeds where applicable
+2. Log all parameters used
+3. Save intermediate files
+4. Export environment info (package versions)
+5. Generate methods section for paper
+"""
+```
+
+These examples demonstrate the breadth of biomedical tasks biomni can handle. Adapt the patterns to your specific research questions, and always include sufficient detail for the agent to execute autonomously.