IDSSP LAB

🏫✨ 歡迎建國中學學生們蒞臨 iDSSP Lab 參訪！

今天很開心迎接來自建國中學的同學們到 iDSSP Lab 進行參訪交流 🤝
透過本次活動，希望同學們能更認識人工智慧、資料科學與智慧醫療等研究方向，也能對未來的學習與研究探索產生更多想像與啟發 💡🔬

謝謝同學們的熱情參與，期待大家都能帶著滿滿收穫回家，也歡迎未來有機會再次交流！🌟

#建國中學 #實驗室參訪

I am thrilled to announce that our paper, "Beyond Curated Knowledge: Structural Protein Embeddings Enhance GNN-Based Personalized Cancer Prognosis," has been accepted by the IEEE Journal of Biomedical and Health Informatics (J-BHI)! 📄✨

While most AI models predict cancer risk by looking at how much a gene is active, we took it a step further: we taught the AI to understand what the protein actually looks like.

By using Protein Language Models (think ChatGPT, but for amino acids), we captured the structural shape of cancer-driving proteins. We then combined this with patient data using Graph Neural Networks to predict 5-year survival outcomes more accurately. This approach helps identify high-risk patients who might need closer monitoring.

A massive thank you and congratulations to the team for their hard work: 👏 Sofia Ormazabal Arriagada 👏 Tsung-Wei Lin 👏 Marta Misztal 👏 Prof. Che Lin

🎉 恭喜 iDSSP Lab 論文獲 ICLR 2026 接受！ 🎉

本研究論文
📄 「Atomic HINs: Entity-Attribute Duality for Heterogeneous Graph Modeling」
已獲
🏆 International Conference on Learning Representations（ICLR 2026） 接受。

本研究提出一種全新的 schema-aware 異質圖建模方法，透過 entity–attribute duality 與 schema-refinement framework，實現對異質資訊網路更系統性且有效的建模方式 🚀

本研究為與中央研究院資訊科技創新研究中心 王志宇（Chih-Yu Wang）教授 之合作成果。誠摯感謝合作夥伴在研究過程中的寶貴討論與支持。

期待在 ICLR 2026 與大家交流！📚✨

🎉 Big congratulations to iDSSP Lab on an ICLR 2026 paper acceptance! 🎉

Our paper
📄 “Atomic HINs: Entity-Attribute Duality for Heterogeneous Graph Modeling”
has been accepted by
🏆 International Conference on Learning Representations (ICLR 2026).

This work introduces a new schema-aware heterogeneous graph modeling paradigm, enabled by entity–attribute duality and a schema-refinement framework, allowing more principled and effective modeling of heterogeneous information networks 🚀

This research was conducted in collaboration with Prof. Chih-Yu Wang at the Research Center for IT Innovation, Academia Sinica. We sincerely thank our collaborators for their insightful discussions and support throughout this work.

Looking forward to sharing and discussing this work at ICLR 2026! 📚✨