기초과학연구원 의생명수학그룹
In this talk, we discuss the paper "scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction" by Z. Liang et al., arxiv, 2025. Abstract This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, …
Complex diseases, such as cancer, sarcopenia, and immune disorders, arise from abnormalities in multiple genes and pathways, posing significant challenges to conventional single-target drug discovery strategies. To address this, we developed a perturbome-based analytical framework that integrates transcriptomic signatures, network pharmacology, and machine learning to identify effective therapeutic candidates. Central to this approach is the …
In this talk, we discuss the paper, "Quantifying interventional causality by knockoff operation" by Xinyan Zhang and Luonan Chen, Science Advances, 2025. Abstract Causal inference between measured variables is crucial to understand the underlying mechanism of complex biological processes at a network level but remains challenging in computational biology. We propose an innovative causal criterion, …
In this talk, we discuss the paper, "A wearable-based aging clock associates with disease and behavior" by A. C. Miller et al., Nature Comm, 2025. Abstract Aging biomarkers play a vital role in understanding longevity, with the potential to improve clinical decisions and interventions. Existing aging clocks typically use blood, vitals, or imaging collected in …
In this talk, we discuss the paper "Generic Temperature Response of Large Biochemical Networks" by Julian B. Voits and Ulrich S. Schwarz, PRX Life, 2025. Abstract Biological systems are remarkably susceptible to relatively small temperature changes. The most obvious example is fever, when a modest rise in body temperature of only few Kelvin has strong …
In this talk, we discuss the paper "Multi-Marginal Flow Matching with Adversarially Learnt Interpolants" by O. Kviman et al., 2025, arxiv. Abstract Learning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajectories are available, but one has only …
Assigned time Chair Topic Mentee Mentor Title 13:30–13:35 Myna Lim Digital Health & Clinical Methodology Suhyeon Hwang Myna Lim Development of a Shortened Version of Cognitive Flexibility Inventory (CFI) 13:35–13:40 Sugwon Cho Myna Lim Machine Learning–Based Development of a Short-Form Scale for Subjective Perceptions of Sleep Medications 13:45–13:50 Taekeun Kim Kangmin Lee Revealing pattern of …
Abstract (국문) 최근 거대언어모델(LLM)을 기술의 발전은 AI4Science 분야에서 Foundation Model 개발에 대한 세계적인 관심을 촉발하였다. 그 중에서도 신약 및 신소재 개발에 연계된 Molecular 도메인에서의 Foundation Model 연구는 막대한 산업적 영향력과 가치를 가지고 있다. 본 발표에서는 분자 구조 생성, 물성, 및 반응 예측 문제에 적용되기 위해 필요한 Multimodal LLM 연구 성과와 방향성을 소개하고자 한다. (English) The …
In this talk, we discuss the paper "Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings" by K. Rahmani et al., Nat. Comm, 2025. Abstract Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells’ electrical properties. Traditional methods like patch-clamp are precise but …
In this talk, we discuss the paper "GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design" by M. Filo et al., arxiv, 2026. Abstract Biomolecular networks underpin emerging technologies in synthetic biology—from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics—and also provide a mechanistic language for understanding complex dynamics in natural …
In this talk, we discuss the paper "TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation" by J.-B. Morlot et al., bioarxiv, 2026. Abstract Drug discovery is impeded by the difficulty of translating targets from preclinical models to patients. Here, we present TwinCell, a Large Causal Cell Model (LCCM) capable of generalising …
In this talk, we discuss the paper "A multimodal sleep foundation model for disease prediction" by Rahul Thapa et al., Nature Medicine, 2026. Abstract Sleep is a fundamental biological process with broad implications for physical and mental health, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich …