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X-WR-CALNAME:Biomedical Mathematics Group
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:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240517T140000
DTEND;TZID=Asia/Seoul:20240517T160000
DTSTAMP:20260424T084001
CREATED:20240428T180844Z
LAST-MODIFIED:20240513T082339Z
UID:9532-1715954400-1715961600@www.ibs.re.kr
SUMMARY:Gyuyoung Hwang\, Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming
DESCRIPTION:We will discuss about “Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming”\, Cell (2019). \n  \nAbstract \nUnderstanding the molecular programs that guide differentiation during development is a major challenge. Here\, we introduce Waddington-OT\, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315\,000 single-cell RNA sequencing (scRNA-seq) profiles\, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent\, extra-embryonic\, and neural cells\, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.
URL:https://www.ibs.re.kr/bimag/event/2024-05-17-jc/
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:20240524T140000
DTEND;TZID=Asia/Seoul:20240524T160000
DTSTAMP:20260424T084001
CREATED:20240428T181352Z
LAST-MODIFIED:20240428T181352Z
UID:9535-1716559200-1716566400@www.ibs.re.kr
SUMMARY:Kévin SPINICCI\, PenDA\, a rank-based method for personalized differential analysis: Application to lung cancer
DESCRIPTION:We will discuss about “PenDA\, a rank-based method for personalized differential analysis: Application to lung cancer” Plos Computational Biology (2020). \nAbstract \n\nThe hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here\, we propose a novel method\, PenDA\, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors\, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts\, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA\, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular\, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust\, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access\, user-friendly R package is available at https://github.com/bcm-uga/penda.
URL:https://www.ibs.re.kr/bimag/event/2024-05-24-jc/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240531T140000
DTEND;TZID=Asia/Seoul:20240531T160000
DTSTAMP:20260424T084001
CREATED:20240428T181746Z
LAST-MODIFIED:20240528T001427Z
UID:9538-1717164000-1717171200@www.ibs.re.kr
SUMMARY:Lucas MacQuarrie\, Data driven governing equations approximation using deep neural networks
DESCRIPTION:We will discuss about “Data driven governing equations approximation using deep neural networks” Journal of Computational Physics (2019). \nAbstract \n\nWe present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular\, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be considered as a one-step method that is exact in temporal integration. We then present two multi-step methods\, recurrent ResNet (RT-ResNet) method and recursive ReNet (RS-ResNet) method. The RT-ResNet is a multi-step method on uniform time steps\, whereas the RS-ResNet is an adaptive multi-step method using variable time steps. All three methods presented here are based on integral form of the underlying dynamical system. As a result\, they do not require time derivative data for equation recovery and can cope with relatively coarsely distributed trajectory data. Several numerical examples are presented to demonstrate the performance of the methods.
URL:https://www.ibs.re.kr/bimag/event/2024-05-31-jc/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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