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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:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231006T140000
DTEND;TZID=Asia/Seoul:20231006T160000
DTSTAMP:20260425T090204
CREATED:20230929T225914Z
LAST-MODIFIED:20231005T021126Z
UID:8564-1696600800-1696608000@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Power spectral estimate for discrete data
DESCRIPTION:We will discuss about “Power spectral estimate for discrete data”\, Nobert Marwan and Tobias Braun\, Chaos (2023). \n  \nAbstract \n\nThe identification of cycles in periodic signals is a ubiquitous problem in time series analysis. Many real-world datasets only record a signal as a series of discrete events or symbols. In some cases\, only a sequence of (non-equidistant) times can be assessed. Many of these signals are furthermore corrupted by noise and offer a limited number of samples\, e.g.\, cardiac signals\, astronomical light curves\, stock market data\, or extreme weather events. We propose a novel method that provides a power spectral estimate for discrete data. The edit distance is a distance measure that allows us to quantify similarities between non-equidistant event sequences of unequal lengths. However\, its potential to quantify the frequency content of discrete signals has so far remained unexplored. We define a measure of serial dependence based on the edit distance\, which can be transformed into a power spectral estimate (EDSPEC)\, analogous to the Wiener–Khinchin theorem for continuous signals. The proposed method is applied to a variety of discrete paradigmatic signals representing random\, correlated\, chaotic\, and periodic occurrences of events. It is effective at detecting periodic cycles even in the presence of noise and for short event series. Finally\, we apply the EDSPEC method to a novel catalog of European atmospheric rivers (ARs). ARs are narrow filaments of extensive water vapor transport in the lower troposphere and can cause hazardous extreme precipitation events. Using the EDSPEC method\, we conduct the first spectral analysis of European ARs\, uncovering seasonal and multi-annual cycles along different spatial domains. The proposed method opens new research avenues in studying of periodic discrete signals in complex real-world systems.
URL:https://www.ibs.re.kr/bimag/event/2023-10-06-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231020T140000
DTEND;TZID=Asia/Seoul:20231020T160000
DTSTAMP:20260425T090204
CREATED:20230929T231212Z
LAST-MODIFIED:20231018T020716Z
UID:8568-1697810400-1697817600@www.ibs.re.kr
SUMMARY:Hyeontae Jo\, AutoScore:A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records
DESCRIPTION:We will discuss about “AutoScore:A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records”\, Xie\, Feng\, et al.\, JMIR medical informatics 8.10 (2020): e21798. \nAbstract\nBackground: Risk scores can be useful in clinical risk stratification and accurate allocations of medical resources\, helping health providers improve patient care. Point-based scores are more understandable and explainable than other complex models and are now widely used in clinical decision making. However\, the development of the risk scoring model is nontrivial and has not yet been systematically presented\, with few studies investigating methods of clinical score generation using electronic health records. \nObjective: This study aims to propose AutoScore\, a machine learning-based automatic clinical score generator consisting of 6 modules for developing interpretable point-based scores. Future users can employ the AutoScore framework to create clinical scores effortlessly in various clinical applications. \nMethods: We proposed the AutoScore framework comprising 6 modules that included variable ranking\, variable transformation\, score derivation\, model selection\, score fine-tuning\, and model evaluation. To demonstrate the performance of AutoScore\, we used data from the Beth Israel Deaconess Medical Center to build a scoring model for mortality prediction and then compared the data with other baseline models using the receiver operating characteristic analysis. A software package in R 3.5.3 (R Foundation) was also developed to demonstrate the implementation of AutoScore. \nResults: Implemented on the data set with 44\,918 individual admission episodes of intensive care\, the AutoScore-created scoring models performed comparably well as other standard methods (ie\, logistic regression\, stepwise regression\, least absolute shrinkage and selection operator\, and random forest) in terms of predictive accuracy and model calibration but required fewer predictors and presented high interpretability and accessibility. The nine-variable\, AutoScore-created\, point-based scoring model achieved an area under the curve (AUC) of 0.780 (95% CI 0.764-0.798)\, whereas the model of logistic regression with 24 variables had an AUC of 0.778 (95% CI 0.760-0.795). Moreover\, the AutoScore framework also drives the clinical research continuum and automation with its integration of all necessary modules. \nConclusions: We developed an easy-to-use\, machine learning-based automatic clinical score generator\, AutoScore; systematically presented its structure; and demonstrated its superiority (predictive performance and interpretability) over other conventional methods using a benchmark database. AutoScore will emerge as a potential scoring tool in various medical applications.
URL:https://www.ibs.re.kr/bimag/event/2023-10-20-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20231027T140000
DTEND;TZID=Asia/Seoul:20231027T160000
DTSTAMP:20260425T090204
CREATED:20230929T230744Z
LAST-MODIFIED:20231018T020236Z
UID:8566-1698415200-1698422400@www.ibs.re.kr
SUMMARY:Hyun Kim\, Significance analysis for clustering with single-cell RNA-sequencing data
DESCRIPTION:We will discuss about “Significance analysis for clustering with single-cell RNA-sequencing data”\, Grabski\, Isabella N.\, Kelly Street\, and Rafael A. Irizarry.\, Nature Methods (2023): 1-7. \nAbstract \n\n\n\nUnsupervised clustering of single-cell RNA-sequencing data enables the identification of distinct cell populations. However\, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty. We find that not addressing known sources of variability in a statistically rigorous manner can lead to overconfidence in the discovery of novel cell types. Here we extend a previous method\, significance of hierarchical clustering\, to propose a model-based hypothesis testing approach that incorporates significance analysis into the clustering algorithm and permits statistical evaluation of clusters as distinct cell populations. We also adapt this approach to permit statistical assessment on the clusters reported by any algorithm. Finally\, we extend these approaches to account for batch structure. We benchmarked our approach against popular clustering workflows\, demonstrating improved performance. To show practical utility\, we applied our approach to the Human Lung Cell Atlas and an atlas of the mouse cerebellar cortex\, identifying several cases of over-clustering and recapitulating experimentally validated cell type definitions.
URL:https://www.ibs.re.kr/bimag/event/2023-10-27-jc/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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