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PRODID:-//Biomedical Mathematics Group - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240904T160000
DTEND;TZID=Asia/Seoul:20240904T170000
DTSTAMP:20260522T061231
CREATED:20240829T001214Z
LAST-MODIFIED:20240830T025822Z
UID:9974-1725465600-1725469200@www.ibs.re.kr
SUMMARY:Quantitative Ecology of Host-associated Microbiomes - Lei Dai
DESCRIPTION:Abstract: The realization that microbiomes\, associated with virtually all multicellular organisms\, have tremendous impact on their host health is considered as one of the most important scientific discoveries in the last decade. The host associated microbiomes\, composed of tens to hundreds of co-existing microbial species\, are highly heterogenous at multiple scales (e.g. between different hosts and within a host). In this talk\, I will share our recent works on understanding the heterogeneity of complex microbial communities\, and how these conceptual and technological advances in microbial ecology pave the way for precision microbiome engineering to prevent and treat diseases.
URL:https://www.ibs.re.kr/bimag/event/quantitative-ecology-of-host-associated-microbiomes/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2024/08/lei-dai-1-e1724986646267.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240905T100000
DTEND;TZID=Asia/Seoul:20240905T110000
DTSTAMP:20260522T061231
CREATED:20240830T085940Z
LAST-MODIFIED:20240904T030529Z
UID:10010-1725530400-1725534000@www.ibs.re.kr
SUMMARY:Make Your Science Friendly: A Guide to Engaging Visuals - Sunghwan Bae
DESCRIPTION:The talk will be hybrid\, participants may join via Zoom with the following link: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09
URL:https://www.ibs.re.kr/bimag/event/make-your-science-friendly-a-guide-to-engaging-visuals-sunghwan-bae/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240906T140000
DTEND;TZID=Asia/Seoul:20240906T160000
DTSTAMP:20260522T061231
CREATED:20240730T001910Z
LAST-MODIFIED:20240904T030852Z
UID:9905-1725631200-1725638400@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Predicting multiple observations in complex systems through low-dimensional embeddings
DESCRIPTION:In this talk\, we discuss the paper\, “Predicting multiple observations in complex systems through low-dimensional embeddings”\, by Tao Wu et. al.\, Nature Communications\, 2024. \nZoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nForecasting all components in complex systems is an open and challenging task\, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework\, namely\, feature-and-reconstructed manifold mapping (FRMM)\, which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system\, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon\, electroencephalogram (EEG) signals\, foreign exchange market\, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor\, and thus has potential for applications in many other real-world systems.
URL:https://www.ibs.re.kr/bimag/event/olive-cawiding-a-flexible-symbolic-regression-method-for-constructing-interpretable-clinical-prediction-models/
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:20240913T140000
DTEND;TZID=Asia/Seoul:20240913T160000
DTSTAMP:20260522T061231
CREATED:20240827T001735Z
LAST-MODIFIED:20240904T030726Z
UID:9958-1726236000-1726243200@www.ibs.re.kr
SUMMARY:Hyun Kim\, Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage
DESCRIPTION:In this talk\, we discuss the paper “Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage” by Zhiwei Huang\, et. al.\, bioRxiv\, 2024. \nZoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nCells must adopt flexible regulatory strategies to make decisions regarding their fate\, including differentiation\, apoptosis\, or survival in the face of various external stimuli. One key cellular strategy that enables these functions is stochastic gene expression programs. However\, understanding how transcriptional bursting\, and consequently\, cell fate\, responds to DNA damage on a genome-wide scale poses a challenge. In this study\, we propose an interpretable and scalable inference framework\, DeepTX\, that leverages deep learning methods to connect mechanistic models and scRNA-seq data\, thereby revealing genome-wide transcriptional burst kinetics. This framework enables rapid and accurate solutions to transcription models and the inference of transcriptional burst kinetics from scRNA-seq data. Applying this framework to several scRNA-seq datasets of DNA-damaging drug treatments\, we observed that fluctuations in transcriptional bursting induced by different drugs could lead to distinct fate decisions: IdU treatment induces differentiation in mouse embryonic stem cells by increasing the burst size of gene expression\, while 5FU treatment with low and high dose increases the burst frequency of gene expression to induce cell apoptosis and survival in human colon cancer cells. Together\, these results show that DeepTX can be used to analyze single-cell transcriptomics data and can provide mechanistic insights into cell fate decisions.
URL:https://www.ibs.re.kr/bimag/event/hyun-kim-deep-learning-linking-mechanistic-models-to-single-cell-transcriptomics-data-reveals-transcriptional-bursting-in-response-to-dna-damage/
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:20240920T140000
DTEND;TZID=Asia/Seoul:20240920T160000
DTSTAMP:20260522T061231
CREATED:20240828T015222Z
LAST-MODIFIED:20240828T015222Z
UID:9966-1726840800-1726848000@www.ibs.re.kr
SUMMARY:Brenda Gavina\, Achieving Occam’s razor: Deep learning for optimal model reduction
DESCRIPTION:In this talk\, we discuss the paper “Achieving Occam’s razor: Deep learning for optimal model reduction” by Botond B. Antal et.al.\, PLOS Computational Biology\, 2024. \nAbstract  \nAll fields of science depend on mathematical models. Occam’s razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data\, and thus inaccurate or ambiguous conclusions. Here\, we show how deep learning can be powerfully leveraged to apply Occam’s razor to model parameters. Our method\, FixFit\, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First\, it provides a metric to quantify the original model’s degree of complexity. Second\, it allows for the unique fitting of data. Third\, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In three use cases\, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system\, we apply FixFit to recover known composite parameters for the Kepler orbit model and a dynamic model of blood glucose regulation. In the latter\, we demonstrate the ability to fit the latent parameters to real data. To illustrate how the method can be applied to less well-established fields\, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.
URL:https://www.ibs.re.kr/bimag/event/brenda-gavina-achieving-occams-razor-deep-learning-for-optimal-model-reduction/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR