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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250905T140000
DTEND;TZID=Asia/Seoul:20250905T160000
DTSTAMP:20260422T213017
CREATED:20250825T080853Z
LAST-MODIFIED:20250901T020216Z
UID:11433-1757080800-1757088000@www.ibs.re.kr
SUMMARY:Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics - Kang Min Lee
DESCRIPTION:In this talk\, we discuss the paper “Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics” by J. Thompson et al.\, bioarxiv\, 2025. \nAbstract \nMicrobial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding\, controlling\, and harnessing their properties. Competition and cross-feeding of metabolites drives microbiome dynamics and functions. Existing mechanistic models that capture metabolite-mediated interactions in microbial communities have limited flexibility due to rigid assumptions. While machine learning models provide flexibility\, they require large datasets\, are challenging to interpret\, and can over-fit to experimental noise. To overcome these limitations\, we develop a physics-constrained machine learning model\, which we call the Neural Species Mediator (NSM)\, that combines a mechanistic model of metabolite dynamics with a machine learning component. The NSM is more accurate than mechanistic or machine learning components on experimental datasets and provides insights into direct biological interactions. In summary\, embedding a neural network into a mechanistic model of microbial community dynamics improves prediction performance and interpretability compared to its constituent mechanistic or machine learning components.
URL:https://www.ibs.re.kr/bimag/event/physics-constrained-neural-ordinary-differential-equation-models-to-discover-and-predict-microbial-community-dynamics-kang-min-lee/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250912T140000
DTEND;TZID=Asia/Seoul:20250912T160000
DTSTAMP:20260422T213017
CREATED:20250825T081619Z
LAST-MODIFIED:20250910T002342Z
UID:11438-1757685600-1757692800@www.ibs.re.kr
SUMMARY:Decomposing causality into its synergistic\, unique\, and redundant components - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Decomposing causality into its synergistic\, unique\, and redundant components” by Álvaro Martínez-Sánchez et al.\, Nature Communications\, 2024. \nAbstract \nCausality lies at the heart of scientific inquiry\, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role\, current methods for causal inference face significant challenges due to nonlinear dependencies\, stochastic interactions\, self-causation\, collider effects\, and influences from exogenous factors\, among others. While existing methods can effectively address some of these challenges\, no single approach has successfully integrated all these aspects. Here\, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant\, unique\, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations\, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods.
URL:https://www.ibs.re.kr/bimag/event/data-driven-model-discovery-and-model-selection-for-noisy-biological-systems-olive-cawiding/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250919T160000
DTEND;TZID=Asia/Seoul:20250919T180000
DTSTAMP:20260422T213017
CREATED:20250825T081133Z
LAST-MODIFIED:20250901T020244Z
UID:11435-1758297600-1758304800@www.ibs.re.kr
SUMMARY:SCassist: An AI Based Workflow Assistant for Single-Cell Analysis - Aqsa Awan
DESCRIPTION:In this talk\, we discuss the paper “SCassist: An AI Based Workflow Assistant for Single-Cell Analysis ” by Vijayaraj Nagarajan et al.\, bioarxiv\, 2025.  \nAbstract \nSingle-cell RNA sequencing (scRNA-seq) data analysis often involves complex iterative workflow\, requiring significant expertise and time. To navigate this complexity\, we have developed SCassist\, an R package that leverages the power of the large language models (LLM’s) to guide and enhance scRNA-seq analysis. SCassist integrates LLM’s into key workflow steps\, to analyze user data and provide relevant recommendations for filtering\, normalization and clustering parameters. It also provides LLM guided insightful interpretations of variable features and principal components\, along with cell type annotations and enrichment analysis. SCassist provides intelligent assistance using popular LLM’s like Google’s Gemini\, OpenAI’s GPT and Meta’s Llama3\, making scRNA-seq analysis accessible to researchers at all levels.
URL:https://www.ibs.re.kr/bimag/event/scassist-an-ai-based-workflow-assistant-for-single-cell-analysis-aqsa-awan/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250926T100000
DTEND;TZID=Asia/Seoul:20250926T113000
DTSTAMP:20260422T213017
CREATED:20250915T083322Z
LAST-MODIFIED:20250924T001304Z
UID:11589-1758880800-1758886200@www.ibs.re.kr
SUMMARY:Tackling inter-subject variability in smartwatch data using factorization models - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “Tackling inter-subject variability in smartwatch data using factorization models” by Arman Naseri et. al\, Scientific Reports\, 2025. \nAbstract \nSmartwatches enable longitudinal and continuous data acquisition. This has the potential to remotely monitor (changes) of the health of users. However\, differences among subjects (inter-subject variability) limit a model to generalize to unseen subjects. This study focused on binary classification tasks using heart rate and step counter from smartwatches\, including night/day and inactive/active classification\, as well as sleep and SpO2-related (oxygen saturation) tasks. To address inter-subject variability\, we explored different transforming and normalization regimes for time series including per-subject and population-based strategies. We propose a modified factorized autoencoder\, which separates the data into two latent spaces capturing class-specific and subject-specific information. Our proposed generalized factorized autoencoder and triplet factorized autoencoder improved classification accuracy over the baseline from 74.8 (± 10.5) to 83.1 (± 5.1) and 83.4 (± 5.3)\, respectively\, for night/day classification\, gains for inactive/active classification were modest\, improving from 84.3 (± 9.4) to 86.9 (± 4.4) and 86.6 (± 4.3)\, respectively. Our study highlights challenges of handling inter-subject variability in smartwatch data and how factorization models can be used to enable more robust and personalized health monitoring solutions for diverse populations.
URL:https://www.ibs.re.kr/bimag/event/tackling-inter-subject-variability-in-smartwatch-data-using-factorization-models-myna-lim/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR