Quick Reference for Brain-Genomics-LLM Integration
Timeline: 2-3 months | Difficulty: Medium | Impact: High
- Integrate pretrained genomic foundation model with LLM
- Enable natural language reasoning over genetic data
- Target: 90%+ accuracy on developmental disorder pathway prediction
Timeline: 4-6 months | Difficulty: High | Impact: Very High
- Contrastive learning on brain imaging + genetics paired data
- Discover novel genetic associations with brain phenotypes
- Requires UK Biobank or equivalent large-scale dataset
Timeline: 6-12 months | Difficulty: Very High | Impact: Transformative
- Med-Gemini style unified architecture
- All modalities: imaging, genomics, clinical notes, trajectories
- Full autonomous scientific discovery capabilities
# Install foundation model libraries
pip install transformers accelerate bitsandbytes
pip install esm fair-esm # For ESM protein models
pip install genslm # For genomic language models
pip install torch torchvision # Latest PyTorch
# For genomics
pip install biopython pysam scikit-allel
# For brain imaging
pip install nibabel nilearn # neuroimaging data
# Clone key repositories
git clone https://github.com/evolutionaryscale/esm.git
git clone https://github.com/bowang-lab/BioReason.git# genomic_foundation_models.py
from transformers import AutoModel, AutoTokenizer
import torch
class GenomicFoundationModels:
"""Load and manage pretrained genomic foundation models"""
def __init__(self):
self.models = {}
self.tokenizers = {}
def load_nucleotide_transformer(self):
"""6-layer transformer pretrained on 850 genomes"""
model_name = "InstaDeepAI/nucleotide-transformer-500m-1000g"
self.models['nt'] = AutoModel.from_pretrained(model_name)
self.tokenizers['nt'] = AutoTokenizer.from_pretrained(model_name)
print("✓ Nucleotide Transformer loaded (500M params)")
def load_esm2_protein(self):
"""Protein language model for genetic variant effects"""
model_name = "facebook/esm2_t33_650M_UR50D"
self.models['esm2'] = AutoModel.from_pretrained(model_name)
self.tokenizers['esm2'] = AutoTokenizer.from_pretrained(model_name)
print("✓ ESM-2 loaded (650M params)")
def load_dnabert2(self):
"""DNA foundation model with BPE tokenization"""
model_name = "zhihan1996/DNABERT-2-117M"
self.models['dnabert2'] = AutoModel.from_pretrained(model_name)
self.tokenizers['dnabert2'] = AutoTokenizer.from_pretrained(model_name)
print("✓ DNABERT-2 loaded (117M params)")
def encode_dna_sequence(self, sequence, model='nt'):
"""Encode DNA sequence to embeddings"""
tokenizer = self.tokenizers[model]
model_obj = self.models[model]
# Tokenize
inputs = tokenizer(sequence, return_tensors="pt", padding=True)
# Get embeddings
with torch.no_grad():
outputs = model_obj(**inputs)
embeddings = outputs.last_hidden_state.mean(dim=1) # Pool
return embeddings
# Usage
gfm = GenomicFoundationModels()
gfm.load_nucleotide_transformer()
gfm.load_esm2_protein()
# Example: Encode a gene sequence
gene_sequence = "ATGCGTACGTAGCTAGCTAG..."
embeddings = gfm.encode_dna_sequence(gene_sequence, model='nt')
print(f"DNA embeddings shape: {embeddings.shape}")# dna_llm_connector.py
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoTokenizer
class DNALLMConnector(nn.Module):
"""BioReason-style connector between DNA foundation model and LLM"""
def __init__(
self,
dna_hidden_dim=512, # Nucleotide Transformer output
llm_hidden_dim=4096, # Qwen/LLaMA hidden size
num_connector_layers=2
):
super().__init__()
# Multi-layer connector with normalization
layers = []
in_dim = dna_hidden_dim
out_dim = llm_hidden_dim
for i in range(num_connector_layers):
layers.extend([
nn.Linear(in_dim, out_dim),
nn.LayerNorm(out_dim),
nn.GELU(),
nn.Dropout(0.1)
])
in_dim = out_dim
self.connector = nn.Sequential(*layers)
def forward(self, dna_embeddings):
"""
Args:
dna_embeddings: [batch, seq_len, dna_hidden_dim]
Returns:
llm_compatible_tokens: [batch, seq_len, llm_hidden_dim]
"""
return self.connector(dna_embeddings)
class DNAToLLM:
"""Complete DNA → LLM inference pipeline"""
def __init__(
self,
dna_model_name="InstaDeepAI/nucleotide-transformer-500m-1000g",
llm_model_name="Qwen/Qwen2.5-7B-Instruct"
):
# Load DNA foundation model
self.dna_model = AutoModel.from_pretrained(dna_model_name)
self.dna_tokenizer = AutoTokenizer.from_pretrained(dna_model_name)
# Load LLM
self.llm = AutoModelForCausalLM.from_pretrained(
llm_model_name,
device_map="auto",
torch_dtype=torch.float16
)
self.llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
# Cross-modal connector
self.connector = DNALLMConnector(
dna_hidden_dim=512,
llm_hidden_dim=self.llm.config.hidden_size
)
def encode_dna(self, sequence):
"""Encode DNA sequence to embeddings"""
inputs = self.dna_tokenizer(sequence, return_tensors="pt", padding=True)
with torch.no_grad():
outputs = self.dna_model(**inputs)
dna_embeddings = outputs.last_hidden_state
return dna_embeddings
def dna_to_llm_tokens(self, dna_sequence):
"""Convert DNA sequence to LLM-compatible tokens"""
# Get DNA embeddings
dna_embeddings = self.encode_dna(dna_sequence)
# Project to LLM space
llm_tokens = self.connector(dna_embeddings)
return llm_tokens
def generate_reasoning(
self,
dna_sequence,
prompt,
max_new_tokens=512
):
"""Generate natural language reasoning about DNA sequence"""
# Convert DNA to LLM tokens
dna_tokens = self.dna_to_llm_tokens(dna_sequence)
# Tokenize text prompt
prompt_ids = self.llm_tokenizer(prompt, return_tensors="pt").input_ids
# Get prompt embeddings
prompt_embeddings = self.llm.get_input_embeddings()(prompt_ids)
# Combine DNA tokens + text prompt
combined_embeddings = torch.cat([dna_tokens, prompt_embeddings], dim=1)
# Generate
with torch.no_grad():
outputs = self.llm.generate(
inputs_embeds=combined_embeddings,
max_new_tokens=max_new_tokens,
temperature=0.7,
do_sample=True
)
# Decode
response = self.llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Usage Example
dna_llm = DNAToLLM()
gene_sequence = "ATGCGTACGTAGCTAGCTAG..." # Your gene sequence
prompt = """
Given the above genetic sequence, explain:
1. What gene this likely represents
2. What protein it encodes
3. Its role in brain development
4. Potential impacts if mutated
Provide step-by-step reasoning.
"""
reasoning = dna_llm.generate_reasoning(gene_sequence, prompt)
print(reasoning)# data_collection.py
class DevelopmentalDisorderDataCollector:
"""Collect and preprocess genetic + phenotype data"""
def __init__(self):
self.data_sources = {
'clinvar': 'https://ftp.ncbi.nlm.nih.gov/pub/clinvar/',
'gwas_catalog': 'https://www.ebi.ac.uk/gwas/',
'gnomad': 'https://gnomad.broadinstitute.org/',
'ukbiobank': 'https://www.ukbiobank.ac.uk/' # Requires access
}
def download_clinvar_variants(self, conditions=['autism', 'ADHD', 'developmental_delay']):
"""Download genetic variants associated with developmental disorders"""
import pandas as pd
# ClinVar API query
variants = []
for condition in conditions:
# Query ClinVar for condition-specific variants
# This is pseudocode - actual implementation needs ClinVar API
url = f"{self.data_sources['clinvar']}?condition={condition}"
df = pd.read_csv(url, sep='\t')
variants.append(df)
combined = pd.concat(variants, ignore_index=True)
return combined
def extract_gene_sequences(self, variants_df):
"""Extract full gene sequences for variants"""
from Bio import Entrez, SeqIO
Entrez.email = "your_email@example.com"
sequences = []
for _, row in variants_df.iterrows():
gene_id = row['GeneID']
# Fetch sequence from NCBI
handle = Entrez.efetch(
db="nucleotide",
id=gene_id,
rettype="fasta",
retmode="text"
)
record = SeqIO.read(handle, "fasta")
sequences.append({
'gene_id': gene_id,
'gene_name': row['GeneName'],
'sequence': str(record.seq),
'condition': row['Condition'],
'clinical_significance': row['ClinicalSignificance']
})
return pd.DataFrame(sequences)
def create_reasoning_dataset(self, sequences_df):
"""Create training data for reasoning (supervised fine-tuning)"""
reasoning_data = []
for _, row in sequences_df.iterrows():
# Template-based reasoning chain
example = {
'sequence': row['sequence'],
'question': f"What is the impact of mutations in {row['gene_name']} on {row['condition']}?",
'reasoning_chain': [
f"Step 1: {row['gene_name']} encodes a protein involved in [function]",
f"Step 2: This protein is critical for [developmental process]",
f"Step 3: Mutations disrupt [specific mechanism]",
f"Step 4: This leads to {row['condition']} through [pathway]"
],
'answer': row['clinical_significance']
}
reasoning_data.append(example)
return reasoning_data
# Usage
collector = DevelopmentalDisorderDataCollector()
variants = collector.download_clinvar_variants()
sequences = collector.extract_gene_sequences(variants)
reasoning_dataset = collector.create_reasoning_dataset(sequences)
print(f"Collected {len(sequences)} gene sequences")
print(f"Created {len(reasoning_dataset)} reasoning examples")# brain_imaging_prep.py
import nibabel as nib
import numpy as np
from nilearn import plotting, image
class BrainImagingProcessor:
"""Process MRI data for foundation model training"""
def __init__(self):
self.idp_extractor = None # Imaging-Derived Phenotypes
def extract_structural_features(self, mri_path):
"""Extract IDPs from T1-weighted MRI (COMICAL-style)"""
# Load MRI
img = nib.load(mri_path)
data = img.get_fdata()
# Extract standard IDPs
idps = {
'total_brain_volume': np.sum(data > 0),
'grey_matter_volume': self._segment_grey_matter(data),
'white_matter_volume': self._segment_white_matter(data),
'cortical_thickness': self._compute_cortical_thickness(data),
'hippocampus_volume': self._segment_hippocampus(data),
'amygdala_volume': self._segment_amygdala(data),
# ... up to 154 IDPs like COMICAL
}
return idps
def _segment_grey_matter(self, data):
"""Segment grey matter (simplified)"""
# Use FSL, FreeSurfer, or deep learning segmentation
# This is pseudocode
grey_matter_mask = (data > 100) & (data < 200)
return np.sum(grey_matter_mask)
def create_brain_genomics_pairs(self, mri_idps, genetic_variants):
"""Create paired data for contrastive learning (COMICAL approach)"""
pairs = []
# For each individual with both imaging and genetics
for subject_id in mri_idps.keys():
if subject_id in genetic_variants:
pair = {
'brain_idps': mri_idps[subject_id], # [154] features
'genetic_snps': genetic_variants[subject_id], # [N] SNPs
'subject_id': subject_id
}
pairs.append(pair)
return pairs
# Usage
processor = BrainImagingProcessor()
idps = processor.extract_structural_features("subject_001_T1.nii.gz")
print(f"Extracted {len(idps)} imaging features")# contrastive_training.py
import torch
import torch.nn as nn
import torch.nn.functional as F
class BrainGenomicsContrastiveModel(nn.Module):
"""COMICAL-style contrastive learning for brain imaging + genomics"""
def __init__(
self,
num_brain_idps=154,
num_snps=10000,
embedding_dim=512
):
super().__init__()
# Brain encoder (transformer for IDPs)
self.brain_encoder = nn.Sequential(
nn.Linear(num_brain_idps, 1024),
nn.LayerNorm(1024),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(1024, embedding_dim)
)
# Genomic encoder (transformer for SNPs)
self.genomic_encoder = nn.Sequential(
nn.Linear(num_snps, 1024),
nn.LayerNorm(1024),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(1024, embedding_dim)
)
# Projection heads
self.brain_proj = nn.Linear(embedding_dim, embedding_dim)
self.genomic_proj = nn.Linear(embedding_dim, embedding_dim)
# Temperature for contrastive loss
self.temperature = nn.Parameter(torch.tensor(0.07))
def forward(self, brain_idps, genetic_snps):
# Encode both modalities
brain_emb = self.brain_encoder(brain_idps)
genomic_emb = self.genomic_encoder(genetic_snps)
# Project to normalized space
brain_emb = F.normalize(self.brain_proj(brain_emb), dim=-1)
genomic_emb = F.normalize(self.genomic_proj(genomic_emb), dim=-1)
return brain_emb, genomic_emb
def contrastive_loss(self, brain_emb, genomic_emb):
"""CLIP-style contrastive loss"""
batch_size = brain_emb.shape[0]
# Compute similarity matrix
similarity = brain_emb @ genomic_emb.T / self.temperature
# Labels: diagonal elements are positive pairs
labels = torch.arange(batch_size, device=brain_emb.device)
# Symmetric loss
loss_brain_to_genomic = F.cross_entropy(similarity, labels)
loss_genomic_to_brain = F.cross_entropy(similarity.T, labels)
return (loss_brain_to_genomic + loss_genomic_to_brain) / 2
def train_contrastive_model(
model,
train_loader,
num_epochs=100,
learning_rate=1e-4
):
"""Training loop"""
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=num_epochs
)
for epoch in range(num_epochs):
model.train()
total_loss = 0
for batch in train_loader:
brain_idps = batch['brain_idps']
genetic_snps = batch['genetic_snps']
# Forward pass
brain_emb, genomic_emb = model(brain_idps, genetic_snps)
# Compute loss
loss = model.contrastive_loss(brain_emb, genomic_emb)
# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
scheduler.step()
avg_loss = total_loss / len(train_loader)
print(f"Epoch {epoch+1}/{num_epochs}, Loss: {avg_loss:.4f}")
return model
# Usage
model = BrainGenomicsContrastiveModel()
# Assuming you have a DataLoader with paired brain-genomics data
# trained_model = train_contrastive_model(model, train_loader)# reasoning_training.py
from transformers import Trainer, TrainingArguments
from datasets import Dataset
class ReasoningTrainer:
"""Train DNA-LLM for multi-step reasoning"""
def __init__(self, dna_llm_model):
self.model = dna_llm_model
def prepare_reasoning_dataset(self, reasoning_data):
"""Convert reasoning examples to training format"""
formatted_data = []
for example in reasoning_data:
# Format: DNA sequence + question + reasoning chain + answer
full_text = f"""
DNA Sequence: {example['sequence']}
Question: {example['question']}
Reasoning:
{chr(10).join(example['reasoning_chain'])}
Answer: {example['answer']}
"""
formatted_data.append({
'text': full_text,
'sequence': example['sequence'],
'question': example['question']
})
return Dataset.from_list(formatted_data)
def train_with_sft(
self,
reasoning_dataset,
output_dir="./dna_llm_reasoning",
num_epochs=3
):
"""Supervised fine-tuning on reasoning chains"""
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-5,
warmup_steps=100,
logging_steps=10,
save_steps=500,
fp16=True, # Mixed precision training
)
trainer = Trainer(
model=self.model.llm,
args=training_args,
train_dataset=reasoning_dataset,
)
trainer.train()
return self.model
def train_with_rl(self, reasoning_dataset):
"""Reinforcement learning for biological coherence (advanced)"""
# This requires implementing a reward model
# Reward = biological correctness + reasoning quality
# Use PPO or similar RL algorithm
# See BioReason paper for details
pass
# Usage
# Assuming you have dna_llm from previous example and reasoning_dataset
trainer = ReasoningTrainer(dna_llm)
dataset = trainer.prepare_reasoning_dataset(reasoning_dataset)
trained_model = trainer.train_with_sft(dataset)# evaluation.py
from sklearn.metrics import accuracy_score, f1_score, recall_score
import numpy as np
class FoundationModelEvaluator:
"""Evaluate foundation model performance"""
def __init__(self):
self.metrics = {}
def evaluate_pathway_prediction(self, predictions, ground_truth):
"""Evaluate on pathway prediction task (BioReason benchmark)"""
accuracy = accuracy_score(ground_truth, predictions)
f1 = f1_score(ground_truth, predictions, average='weighted')
recall = recall_score(ground_truth, predictions, average='weighted')
print(f"Pathway Prediction Accuracy: {accuracy:.3f}")
print(f"F1 Score: {f1:.3f}")
print(f"Recall: {recall:.3f}")
# BioReason achieved 98% accuracy - this is the target
if accuracy >= 0.90:
print("✓ Achieved target performance (>90%)")
else:
print(f"✗ Need improvement: {0.90 - accuracy:.3f} below target")
return {'accuracy': accuracy, 'f1': f1, 'recall': recall}
def evaluate_cross_modal_retrieval(
self,
brain_embeddings,
genomic_embeddings,
k=10
):
"""Evaluate retrieval performance (COMICAL benchmark)"""
# Compute similarity matrix
similarity = brain_embeddings @ genomic_embeddings.T
# Recall@K
top_k = torch.topk(similarity, k, dim=1).indices
correct_in_top_k = torch.any(top_k == torch.arange(len(similarity)).unsqueeze(1), dim=1)
recall_at_k = correct_in_top_k.float().mean().item()
print(f"Recall@{k}: {recall_at_k:.3f}")
# Target: >0.8 for production
if recall_at_k >= 0.8:
print("✓ Achieved target retrieval performance")
else:
print(f"✗ Need improvement: {0.8 - recall_at_k:.3f} below target")
return {'recall_at_k': recall_at_k}
def evaluate_reasoning_quality(self, generated_reasoning, expert_annotations):
"""Evaluate reasoning chain quality"""
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
# Compute semantic similarity
generated_emb = model.encode(generated_reasoning)
expert_emb = model.encode(expert_annotations)
similarity = util.pytorch_cos_sim(generated_emb, expert_emb)
# Faithfulness score
faithfulness = similarity.mean().item()
print(f"Reasoning Faithfulness: {faithfulness:.3f}")
# Target: >0.8 for production
if faithfulness >= 0.8:
print("✓ High-quality reasoning")
else:
print(f"✗ Need improvement in reasoning quality")
return {'faithfulness': faithfulness}
# Usage
evaluator = FoundationModelEvaluator()
# Example evaluation
predictions = [0, 1, 1, 0, 1] # Predicted pathways
ground_truth = [0, 1, 1, 1, 1] # True pathways
metrics = evaluator.evaluate_pathway_prediction(predictions, ground_truth)# foundation_rag_integration.py
from src.services.rag.unified_rag_orchestrator import UnifiedRAGOrchestrator
from src.services.rag.rag_strategy_base import RAGStrategy
class FoundationModelRAGStrategy(RAGStrategy):
"""New RAG strategy using foundation models"""
def __init__(self, genomic_model, brain_model):
self.genomic_model = genomic_model
self.brain_model = brain_model
async def retrieve(self, query, top_k=5):
"""Multi-modal retrieval using foundation models"""
# Classify query type
if self._is_genomic_query(query):
return await self._retrieve_genomic(query, top_k)
elif self._is_brain_imaging_query(query):
return await self._retrieve_brain(query, top_k)
else:
return await self._retrieve_multimodal(query, top_k)
async def _retrieve_genomic(self, query, top_k):
"""Genomic-specific retrieval"""
# Encode query with genomic foundation model
query_embedding = self.genomic_model.encode(query)
# Search in genomic vector store
results = await self.vector_store.similarity_search(
query_embedding,
collection="genomics",
top_k=top_k
)
return results
async def _retrieve_brain(self, query, top_k):
"""Brain imaging-specific retrieval"""
query_embedding = self.brain_model.encode(query)
results = await self.vector_store.similarity_search(
query_embedding,
collection="brain_imaging",
top_k=top_k
)
return results
async def _retrieve_multimodal(self, query, top_k):
"""Cross-modal retrieval"""
# Retrieve from both modalities
genomic_results = await self._retrieve_genomic(query, top_k)
brain_results = await self._retrieve_brain(query, top_k)
# Combine and re-rank
combined = self._combine_results(genomic_results, brain_results)
return combined[:top_k]
# Integration with existing orchestrator
class EnhancedRAGOrchestrator(UnifiedRAGOrchestrator):
"""Extend existing orchestrator with foundation models"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Add foundation model strategy
self.strategies['foundation_model'] = FoundationModelRAGStrategy(
genomic_model=load_genomic_foundation_model(),
brain_model=load_brain_foundation_model()
)
async def query_with_reasoning(self, query):
"""Enhanced query with multi-step reasoning"""
# Step 1: Retrieve with foundation models
contexts = await self.strategies['foundation_model'].retrieve(query)
# Step 2: Multi-hop reasoning
reasoning_chain = await self._multi_hop_reasoning(query, contexts)
# Step 3: Generate answer with citations
answer = await self._generate_with_citations(
query, reasoning_chain, contexts
)
return {
'answer': answer,
'reasoning': reasoning_chain,
'contexts': contexts
}# foundation_agent_integration.py
from src.agents.pool import AgentPool, ResearchAgent
class GenomicFoundationAgent(ResearchAgent):
"""New agent using genomic foundation models"""
def __init__(self):
super().__init__(
name="GenomicFoundationAgent",
capabilities=[
"genetic_variant_analysis",
"pathway_prediction",
"multi_step_reasoning"
]
)
self.dna_llm = DNAToLLM() # From earlier example
async def process_task(self, task):
"""Process task using foundation model"""
if task.type == "analyze_variant":
return await self._analyze_genetic_variant(task.data)
elif task.type == "predict_pathway":
return await self._predict_disease_pathway(task.data)
else:
return await super().process_task(task)
async def _analyze_genetic_variant(self, variant_data):
"""Analyze genetic variant using DNA-LLM"""
sequence = variant_data['sequence']
prompt = f"""
Analyze this genetic variant:
Gene: {variant_data['gene']}
Position: {variant_data['position']}
Change: {variant_data['change']}
Provide:
1. Predicted impact on protein function
2. Relevance to brain development
3. Association with developmental disorders
4. Confidence score
Use step-by-step reasoning.
"""
reasoning = self.dna_llm.generate_reasoning(sequence, prompt)
return {
'analysis': reasoning,
'agent': self.name,
'confidence': self._extract_confidence(reasoning)
}
# Add to existing agent pool
enhanced_pool = AgentPool()
enhanced_pool.register_agent(GenomicFoundationAgent())
# Now the pool can route genomic tasks to foundation model agent# model_optimization.py
from transformers import AutoModelForCausalLM
import torch
class ModelOptimizer:
"""Optimize models for production deployment"""
@staticmethod
def quantize_model(model, bits=8):
"""Quantize model to reduce memory and increase speed"""
from transformers import BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_8bit=True if bits == 8 else False,
load_in_4bit=True if bits == 4 else False,
bnb_4bit_compute_dtype=torch.float16
)
quantized_model = AutoModelForCausalLM.from_pretrained(
model.name_or_path,
quantization_config=quantization_config,
device_map="auto"
)
return quantized_model
@staticmethod
def apply_lora(model, rank=16):
"""Apply LoRA for parameter-efficient fine-tuning"""
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=rank,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.1,
bias="none",
task_type="CAUSAL_LM"
)
lora_model = get_peft_model(model, lora_config)
return lora_model
@staticmethod
def optimize_inference(model):
"""Optimize for inference speed"""
# Use vLLM for 3× speedup (GenomeOcean result)
# Compile with torch.compile (PyTorch 2.0+)
compiled_model = torch.compile(model, mode="reduce-overhead")
return compiled_model
# Usage
optimizer = ModelOptimizer()
optimized_model = optimizer.quantize_model(model, bits=8)
lora_model = optimizer.apply_lora(optimized_model, rank=16)# api_endpoints.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
app = FastAPI()
class GenomicQuery(BaseModel):
sequence: str
question: str
max_length: int = 512
class BrainGenomicsQuery(BaseModel):
brain_idps: list[float]
genetic_snps: list[int]
query: str
@app.post("/api/v1/genomic-reasoning")
async def genomic_reasoning(query: GenomicQuery):
"""Endpoint for DNA-LLM reasoning"""
try:
# Load model (cache this in production)
dna_llm = DNAToLLM()
# Generate reasoning
reasoning = dna_llm.generate_reasoning(
query.sequence,
query.question,
max_new_tokens=query.max_length
)
return {
"reasoning": reasoning,
"sequence_length": len(query.sequence),
"model": "DNA-LLM-v1"
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/v1/brain-genomics-search")
async def brain_genomics_search(query: BrainGenomicsQuery):
"""Endpoint for cross-modal brain-genomics retrieval"""
try:
# Load COMICAL-style model
model = BrainGenomicsContrastiveModel()
model.load_state_dict(torch.load("comical_model.pt"))
# Encode inputs
brain_emb, genomic_emb = model(
torch.tensor([query.brain_idps]),
torch.tensor([query.genetic_snps])
)
# Search for similar cases
similar_cases = search_database(brain_emb, genomic_emb, query.query)
return {
"similar_cases": similar_cases,
"query": query.query
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))# monitoring.py
from prometheus_client import Counter, Histogram, Gauge
import time
# Metrics
pathway_prediction_accuracy = Gauge(
'pathway_prediction_accuracy',
'Accuracy on disease pathway prediction'
)
cross_modal_retrieval_recall = Gauge(
'cross_modal_retrieval_recall_at_10',
'Recall@10 for cross-modal retrieval'
)
reasoning_quality = Gauge(
'reasoning_faithfulness',
'Faithfulness score of generated reasoning'
)
inference_latency = Histogram(
'inference_latency_seconds',
'Inference latency in seconds'
)
class PerformanceMonitor:
"""Monitor foundation model performance"""
def __init__(self):
self.targets = {
'pathway_accuracy': 0.90, # BioReason: 98%
'retrieval_recall': 0.80,
'reasoning_quality': 0.80,
'inference_latency': 2.0 # seconds
}
def check_performance(self, metrics):
"""Check if metrics meet targets"""
status = {}
for metric_name, target in self.targets.items():
current = metrics.get(metric_name, 0)
meets_target = current >= target
status[metric_name] = {
'current': current,
'target': target,
'status': 'PASS' if meets_target else 'FAIL',
'gap': target - current if not meets_target else 0
}
# Update Prometheus metrics
if metric_name == 'pathway_accuracy':
pathway_prediction_accuracy.set(current)
elif metric_name == 'retrieval_recall':
cross_modal_retrieval_recall.set(current)
elif metric_name == 'reasoning_quality':
reasoning_quality.set(current)
return status
def log_performance(self, status):
"""Log performance status"""
print("\n=== Performance Status ===")
for metric, data in status.items():
icon = "✓" if data['status'] == 'PASS' else "✗"
print(f"{icon} {metric}: {data['current']:.3f} (target: {data['target']:.3f})")
if data['gap'] > 0:
print(f" → Need {data['gap']:.3f} improvement")
# Usage
monitor = PerformanceMonitor()
# After evaluation
metrics = {
'pathway_accuracy': 0.92,
'retrieval_recall': 0.85,
'reasoning_quality': 0.78,
'inference_latency': 1.5
}
status = monitor.check_performance(metrics)
monitor.log_performance(status)- Week 1: Environment setup, model loading
- Week 2: Data collection and preprocessing
- Week 3: Basic inference pipeline
- Week 4: Initial evaluation framework
Deliverable: Working prototype with pretrained models
- Week 5-6: Train contrastive brain-genomics model
- Week 7-8: Fine-tune for reasoning
Deliverable: Custom foundation model for developmental disorders
- Week 9: Integrate with AI-CoScientist RAG
- Week 10: Agent pool integration
- Week 11: API development
- Week 12: Production deployment
Deliverable: Production-ready system with monitoring
Issue: Out of memory during training Solution: Use gradient checkpointing, reduce batch size, apply quantization
model.gradient_checkpointing_enable()
training_args.per_device_train_batch_size = 1
training_args.gradient_accumulation_steps = 16Issue: Slow inference Solution: Use vLLM, quantization, model compilation
from vllm import LLM
llm = LLM(model="your-model", tensor_parallel_size=2)Issue: Poor reasoning quality Solution: Increase supervised fine-tuning data, add reinforcement learning
# Collect more expert-annotated reasoning examples
# Increase diversity of training examples
# Add reward model for biological coherence- Nucleotide Transformer:
InstaDeepAI/nucleotide-transformer-500m-1000g - ESM-2:
facebook/esm2_t33_650M_UR50D - DNABERT-2:
zhihan1996/DNABERT-2-117M - Qwen:
Qwen/Qwen2.5-7B-Instruct
- ClinVar: https://ftp.ncbi.nlm.nih.gov/pub/clinvar/
- GWAS Catalog: https://www.ebi.ac.uk/gwas/
- UK Biobank: https://www.ukbiobank.ac.uk/
- KEGG Pathways: https://www.genome.jp/kegg/pathway.html
- BioReason: https://arxiv.org/abs/2505.23579
- COMICAL: https://www.medrxiv.org/content/10.1101/2024.11.02.24316653v1
- Med-Gemini: https://arxiv.org/abs/2405.03162
Document Version: 1.0 Last Updated: December 8, 2025 Estimated Implementation Time: 2-3 months for BioReason pathway, 4-6 months for COMICAL pathway