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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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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240906T140000
DTEND;TZID=Asia/Seoul:20240906T160000
DTSTAMP:20260424T014454
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:20260424T014454
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:20260424T014454
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
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