PREDIKTOR: Patient-centered multi-view framework aligning personalized knowledge graphs with gene-level perturbation representations for clinical drug response prediction. Combines DysRegNet GRN construction, DrugBank integration, GNN encoding, LINCS L1000 pretraining, and CLIP-style contrastive alignment.
PREDIKTOR: Patient Knowledge Graph Drug Response
Description
PREDIKTOR is a patient-centered multi-view framework that aligns a personalized network view (individualized GRN + drug-target knowledge) with a transferable transcriptomic perturbation view to predict clinical drug response. It integrates DysRegNet for patient-specific GRN construction, DrugBank for drug-target links, GNN encoding for mechanistically grounded embeddings, a frozen LINCS L1000 attention model for simulated post-perturbation profiles, and CLIP-style contrastive alignment in a shared latent space.
Activation Keywords
prediktor
patient knowledge graph drug response
personalized drug response prediction
患者知识图谱药物响应
个性化药物响应预测
GRN drug response
transcriptomic perturbation alignment
precision oncology prediction
patient-specific therapeutic response
multi-view clinical prediction
DysRegNet drug prediction
CLIP alignment drug response
I-SPY2 trial prediction
patient-specific GRN drug
Tools Used
web_search: Search arXiv for related papers
web_extract: Fetch paper details
write: Create analysis scripts or reports
exec: Run Python for GNN/ML pipelines
Core Methodology
Two-View Architecture
Network View (Mechanistic)
Build patient-specific GRN from tumor expression using DysRegNet
Augment GRN with drug-target links from DrugBank
Encode with Graph Neural Network → mechanistically grounded embedding
Perturbation View (Transferable)
Frozen condition-specific gene-gene attention model pretrained on LINCS L1000
Generates simulated post-perturbation transcriptomic profile for patient-drug pair
Captures drug-induced transcriptomic dynamics
CLIP-Style Contrastive Alignment
Align both views in shared latent space
Use contrastive objective with drug-context hard negatives
Concatenate aligned representations for end-to-end response classification
Hard negatives ensure model learns drug-specific discrimination
Key Results
Consistently outperforms state-of-the-art baselines on TCGA
Patient-split, drug-split, and tissue-split evaluations all show improvement
Zero-shot transfer to I-SPY2 trial: +5.6% AUROC over competing methods
Aligned embeddings yield stable gene and pathway attributions
Recovers known mechanisms, supporting actionable precision oncology
Usage Patterns
Pattern 1: Clinical Drug Response Prediction
For each patient, construct individualized GRN from tumor expression (DysRegNet)
Align both representations via contrastive learning
Classify drug response using concatenated aligned embeddings
Pattern 2: Zero-Shot Transfer to New Trials
Train on source dataset (e.g., TCGA) with multi-view alignment
Apply to unseen trial data (e.g., I-SPY2) without fine-tuning
Benefit from mechanistic grounding + perturbation transferability
Pattern 3: Interpretable Attributions
Extract aligned embeddings from trained model
Compute gene and pathway attributions
Verify known drug mechanisms are recovered
Use attributions to guide clinical decision-making
Step-by-Step Implementation Guide
Step 1: Patient GRN Construction
# For each patient tumor sample# Use DysRegNet to infer individualized gene regulatory network# DysRegNet handles dysregulated GRN inference from expression data
grn = dysregnet_infer(tumor_expression, patient_id)
# Encode augmented GRN into embedding space
grn_embedding = gnn_encoder(grn_drug)
# Captures structural + mechanistic information
Step 4: Perturbation Profile Generation
# Frozen LINCS L1000 attention model# Simulates post-perturbation transcriptomic profile
perturbation_profile = lincs_attention_model(
patient_expression, drug_id, condition
)
# Model is frozen — no gradient updates during training
Step 5: CLIP-Style Alignment
# Contrastive objective with hard negatives# Hard negatives: same drug, different patient context
loss = clip_contrastive_loss(
grn_embedding, perturbation_profile,
hard_negatives=drug_context_negatives
)
Step 6: Response Classification
# Concatenate aligned representations
combined = concat(grn_embedding, perturbation_profile)
response_prediction = classifier(combined)
# Binary or multi-class: responder vs non-responder
Error Handling
Limited Training Labels
Problem: Matched clinical response labels are scarce
Solution: Pretrain perturbation view on large-scale LINCS L1000 data (no clinical labels needed)
Cross-Site Generalization
Problem: Models trained on one site may not generalize
Solution: Multi-view alignment provides robustness; perturbations are transferable
Static Knowledge Graph Limitation
Problem: DrugBank is static, doesn't capture dynamics
Solution: Perturbation view compensates with dynamic transcriptomic simulation
