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PRODID:-//Biomedical Mathematics Group - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220822T140000
DTEND;TZID=Asia/Seoul:20220822T144000
DTSTAMP:20260518T094002
CREATED:20220817T124201Z
LAST-MODIFIED:20220817T124201Z
UID:6403-1661176800-1661179200@www.ibs.re.kr
SUMMARY:[Big data analysis for complex biological systems\, 1/3] A Bayesian Convolutional Neural Network-based Generalized Linear Model
DESCRIPTION:Abstract: Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy\, statistical inference such as estimating the effects of covariates and quantifying the prediction uncertainty is not trivial due to the highly complicated model structure and overparameterization. To address this challenge\, we propose a new Bayes approach by embedding CNNs within the generalized linear model (GLM) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo dropout as informative covariates in GLM. This improves prediction accuracy and provides an interpretation of regression coefficients. By fitting ensemble GLMs across multiple realizations from Monte Carlo dropout\, we can fully account for uncertainties in model estimation. We apply our methods to simulated and real data examples\, including non-Gaussian spatial data\, brain tumor image data\, and fMRI data. The algorithm can be broadly applicable to image regressions or correlated data analysis by providing accurate Bayesian inference quickly.
URL:https://www.ibs.re.kr/bimag/event/2022-08-22-workshop1/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Workshops and Conferences
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220822T150000
DTEND;TZID=Asia/Seoul:20220822T154000
DTSTAMP:20260518T094002
CREATED:20220817T125332Z
LAST-MODIFIED:20220817T125332Z
UID:6407-1661180400-1661182800@www.ibs.re.kr
SUMMARY:[Big data analysis for complex biological systems\, 2/3] DCLEAR: Reconstructing Single Cell Lineage Trees from CRISPR recorders by Distance-based Methods
DESCRIPTION:Abstract: A fundamental challenge in biology is the reconstruction of developmental trajectories as they divide and progress through different stages. The recent advance of CRISPR-based molecular tools\, such as intMEMOIR and scGESTALT\, have produced a new generation of techniques that enable the reconstruction of cell lineages of complex organisms at single-cell resolution. However\, there are significant challenges for computationally inferring the lineage trees upon the noisy experimental readout at the single cell level. The recent Allen Institute lineage reconstruction DREAM challenge was the first attempt to rigorously examine the performance and robustness of lineage reconstruction algorithms by using benchmark experimental and in silico data. We proposed two novel methods weighted hamming distance and k-mer replacement distance approaches of estimating the cell distances from the CRISPR/Cas9-enabled recorders and outperform existing methods by several metrics and under a wide variety of parameters regimes. Our methods won subchallenge 2 and 3 of the competition. Our new algorithms should enable the accurate large-scale lineage tracing efforts. The weighted hamming distance and k-mer replacement distance methods were implemented as an R package DCLEAR (https://github.com/ikwak2/DCLEAR).
URL:https://www.ibs.re.kr/bimag/event/2022-08-16-workshop2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Workshops and Conferences
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220822T160000
DTEND;TZID=Asia/Seoul:20220822T173000
DTSTAMP:20260518T094002
CREATED:20220817T130351Z
LAST-MODIFIED:20220817T130351Z
UID:6410-1661184000-1661189400@www.ibs.re.kr
SUMMARY:[Big data analysis for complex biological systems\, 3/3] Short presentation session
DESCRIPTION:In this short presentation session\, six researchers from IBS BIMAG present 10-minute talks. \n4:00 — 4:10\, Hyeontae Jo\n4:15 — 4:25\, Hyun Kim\n4:30 — 4:40\, Aurelio de los Reyes V\n4:45 — 4:55\, Yun Min Song\n5:00 — 5:10\, Seokmin Ha\n5:15 — 5:25\, Hyukpyo Hong
URL:https://www.ibs.re.kr/bimag/event/2022-08-22-workshop3/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Workshops and Conferences
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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