Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations

2026-07-06 · Source: Takara TLDR - Daily AI Papers · Field: Health & Wellbeing — Pharmaceuticals & Biotechnology, Clinical Care & Medical Practice, Health & Medical Research · Depth: Expert, medium

Summary

PREDIKTOR is a novel patient-centered multi-view framework designed to accurately predict patient-specific therapeutic response from pre-treatment transcriptomes. It addresses limitations of existing methods by aligning two distinct data representations. First, it constructs an individualized gene regulatory network for each patient using DysRegNet and DrugBank, processed by a graph neural encoder to create a drug-centric embedding. Second, a frozen gene-gene attention model, pretrained on LINCS L1000, simulates a post-perturbation transcriptomic profile. These two views are aligned in a shared latent space using a CLIP-style contrastive objective with drug-context hard negatives. The combined representations are then used for end-to-end response classification. PREDIKTOR consistently outperforms leading baselines on TCGA datasets and achieves a 5.6% AUROC improvement over competing methods in zero-shot transfer to the I-SPY2 trial, providing stable and interpretable gene and pathway attributions for precision oncology.

Key takeaway

For AI Scientists and Research Scientists developing precision oncology models, PREDIKTOR offers a robust framework for predicting therapeutic outcomes. You should consider integrating patient-specific gene regulatory networks with simulated drug-induced transcriptomic profiles, aligning these views via contrastive learning. This approach not only improves prediction accuracy, demonstrated by a 5.6% AUROC gain on I-SPY2, but also provides stable, interpretable gene and pathway attributions, crucial for understanding drug mechanisms and informing clinical decisions.

Key insights

Aligning patient-specific knowledge graphs with gene-level perturbation representations accurately predicts therapeutic outcomes and provides mechanistic interpretability.

Principles

• Multi-view alignment improves prediction and interpretability.
• Combine static knowledge graphs with dynamic perturbation models.
• Contrastive learning effectively aligns diverse biological data views.

Method

Construct patient-specific gene regulatory networks, simulate post-perturbation transcriptomes, align these views via a CLIP-style contrastive objective, then classify responses.

In practice

• Apply DysRegNet for individualized gene networks.
• Pretrain models on LINCS L1000 for perturbation simulation.
• Utilize CLIP-style contrastive learning for multi-modal biological data.

Topics

• Precision Oncology
• Therapeutic Outcome Prediction
• Knowledge Graphs
• Gene Regulatory Networks
• Transcriptomics
• Contrastive Learning

Code references

• AI4Science-WestlakeU/scDFM

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.