Teerapon Jung

DeepSeek-R1 is great, but it still does not handle genomics yet :) 🚀 We’re thrilled to announce that IntegrAO (pronounced "integral") 🧬🔬—our new multi-omics integration framework—has been published in Nature Machine Intelligence! 📑 Paper: nature.com/articles/s42256-0… 💻 Code: github.com/bowang-lab/Integr… 🔬 What is IntegrAO? IntegrAO (Integrate Any Omics) is an unsupervised platform designed to tackle the challenges of incomplete multi-omics data. Crucially, it can be seamlessly transformed into a prediction model after integration, enabling robust classification of new patient samples—even when only partial omics data is available. 🧠 Why IntegrAO? Effective cancer stratification depends on comprehensive multiomics data, yet real-world clinical datasets are often incomplete. Accurately assigning new patients to predefined subtypes, even when only partial omics are available, is crucial for personalized care but remains a major barrier to the clinical adoption of multiomics. Existing methods struggle with missing data and rarely address the essential task of classifying patients with partial profiles. IntegrAO excels at overcoming these challenges. 🌟 IntegrAO Highlights: - Transductive Integration: Integrates patient graphs with partial overlaps across any omics sources. Utilizes GNNs to produce unified patient embeddings, preserving critical biological and clinical signals. - Resilience to Missing Data: Demonstrates robust performance on heterogeneous, incomplete datasets from multiple cancer cohorts. Maintains accuracy despite varying degrees of missing omics. - Inductive Prediction: Converts into a prediction model via supervised fine-tuning. Accurately classifying new samples with partial data into existing subtypes is crucial for precision medicine. - Real-World Impact: Comprehensive case study on acute myeloid leukemia, revealing clinically and biologically relevant subgroups. Shoutout to Rex Ma (@RexMa9), an incredible PhD student in the lab, for his exceptional leadership on this project! Huge thanks as well to our amazing collaborators: Andy G. X. Zeng, Benjamin Haibe-Kains, Anna Goldenberg, and John E. Dick! 👏🎉 @VectorInst @UHN_Research @UofTCompSci @UofT_TCAIREM