BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//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:20240607T140000
DTEND;TZID=Asia/Seoul:20240607T160000
DTSTAMP:20260424T085924
CREATED:20240531T044227Z
LAST-MODIFIED:20240606T054542Z
UID:9650-1717768800-1717776000@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe
DESCRIPTION:In this talk\, we discuss the paper “Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe”\, by Xiaojie Qiu  et.al.\, Cell Syst. 2020. \nAbstract  \nHere\, we present Scribe (https://github.com/aristoteleo/Scribe-py)\, a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for “pseudotime”-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as “RNA velocity” restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.
URL:https://www.ibs.re.kr/bimag/event/olive-cawiding-causalxtract-a-flexible-pipeline-to-extract-causal-effects-from-live-cell-time-lapse-imaging-data/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240614T140000
DTEND;TZID=Asia/Seoul:20240614T160000
DTSTAMP:20260424T085924
CREATED:20240531T044753Z
LAST-MODIFIED:20240614T002219Z
UID:9652-1718373600-1718380800@www.ibs.re.kr
SUMMARY:Hyun Kim\, MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing datamics data with TDEseq
DESCRIPTION:In this talk\, we discuss the paper\, “MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data” by Siyao Liu et.al.  Genome Biology\, 2024. \nAbstract  \nSingle-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations\, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data\, thus providing an objective means to estimating the number of possible groups or cell-type populations present. \n 
URL:https://www.ibs.re.kr/bimag/event/hyun-kim-powerful-and-accurate-detection-of-temporal-gene-expression-patterns-from-multi-sample-multi-stage-single-cell-transcriptomics-data-with-tdeseq/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240621T140000
DTEND;TZID=Asia/Seoul:20240621T160000
DTSTAMP:20260424T085924
CREATED:20240531T045615Z
LAST-MODIFIED:20240620T065839Z
UID:9654-1718978400-1718985600@www.ibs.re.kr
SUMMARY:Brenda Gavina\, A modified shuffled frog leaping algorithm with inertia weight
DESCRIPTION:In this talk\, we will discuss the paper\, “A modified shuffled frog leaping algorithm with inertia weight”\, by Zhuanzhe Zhao et.al. \, Scientific Reports\, 2024. \nAbstract  \nThe shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm\, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However\, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings\, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA\, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally\, we have compared the solution of the 7 benchmark functions with the original SFLA\, other improved SFLAs\, genetic algorithm\, PSO\, artificial bee colony algorithm\, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property\, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.
URL:https://www.ibs.re.kr/bimag/event/brenda-gavina-computational-screen-for-sex-specific-drug-effects-in-a-cardiac-fibroblast-signaling-network-model/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR