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X-WR-CALNAME:Institute for Quantitative and Computational Biosciences
X-ORIGINAL-URL:https://qcb.ucla.edu
X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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DTSTART;TZID=America/Los_Angeles:20231201T130000
DTEND;TZID=America/Los_Angeles:20231201T133000
DTSTAMP:20231201T222154Z
CREATED:20231030T181641Z
LAST-MODIFIED:20231201T222154Z
UID:25976-1701435600-1701437400@qcb.ucla.edu
SUMMARY:Research-in-Progress (RIP) Seminar: Kaija Gahm (Pinter-Wollman)\, Graduate Student in Ecology & Evolutionary Biology
DESCRIPTION:TITLE: “An updated movement path randomization method to distinguish social and spatial drivers of animal interactions.” \nABSTRACT: Studying the spatial-social interface requires tools that distinguish between social and spatial drivers of interactions. Testing hypotheses regarding the factors determining animal interactions often involves comparing observed interactions with reference or ’null’ models. One approach to constructing reference models that account for spatial drivers of social interactions is randomizing animal movement paths to decouple their spatial and social phenotypes while maintaining environmental effects on movements. Here we propose a new randomization approach. Using agent-based simulations\, we explore the utility of the new approach for different types of animal movements and compare its performance to existing approaches. We show that our method provides reference models that are more similar to the original tracking data\, while still distinguishing between social and spatial drivers. Furthermore\, the new approach results in fewer false-positives than other approaches\, especially when animals do not return to the same place each night but change movement foci\, either locally or directionally. Finally\, we show that interactions among GPStracked griffon vultures (Gyps fulvus) emerge from social attraction rather than from their movement patterns alone. We conclude by highlighting the biological situations in which the new method might be most suitable for testing hypotheses about the underlying causes of social interactions. \n\nhttps://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2023/10/Kaija-Gahm.mp4
URL:https://qcb.ucla.edu/event/research-in-progress-rip-seminar-kaija-gahm-pinter-wollman-graduate-student-in-ecology-evolutionary-biology/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:QCBio Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://qcb.ucla.edu/wp-content/uploads/sites/14/2023/10/Kaija_Headshot_Final-3.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231201T133000
DTEND;TZID=America/Los_Angeles:20231201T140000
DTSTAMP:20231201T221917Z
CREATED:20231018T093230Z
LAST-MODIFIED:20231201T221917Z
UID:25893-1701437400-1701439200@qcb.ucla.edu
SUMMARY:Research-in-Progress (RIP) Seminar: Samir Akre (Bui)\, Graduate Student in Medical Informatics
DESCRIPTION:TITLE: “Detection of Symptoms of Depression Using Data From the iPhone and Apple Watch.” \nABSTRACT: Digital health data from consumer wearable devices and smartphones have the potential to improve our understanding of mental illness. However\, in conditions like depression\, there is not yet a consistent uniform measurement tool whose result can be reliably used as a gold standard measure of depression severity. This work seeks to specify what symptoms and dimensions of depression can be detected using vitals\, activity\, and sleep monitored by consumer wearable devices. Machine learning models are fit to digital health data and used to detect responses to individual questions from self-reports as well as summary scores. Data is analyzed from an ongoing study with data from the Apple Watch\, iPhone\, and validated self-reports. The digital health data investigated was found to detect depression severity and specific symptoms like poor appetite\, aspects of anhedonia\, and sleep timings (ROC AUC 0.63 to 0.72). \nhttps://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2023/10/Samir-Akre.mp4
URL:https://qcb.ucla.edu/event/research-in-progress-rip-seminar-samir-akre-bui-graduate-student-in-medical-informatics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:QCBio Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://qcb.ucla.edu/wp-content/uploads/sites/14/2023/10/Akre-Samir-AMIA2022_crop.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231204T120000
DTEND;TZID=America/Los_Angeles:20231204T130000
DTSTAMP:20231017T092236Z
CREATED:20231017T092149Z
LAST-MODIFIED:20231017T092236Z
UID:25854-1701691200-1701694800@qcb.ucla.edu
SUMMARY:Frontiers in Computational Biosciences Seminar Series: Monday\, December 4\, 2023  Graciela Gonzalez-Hernandez\, PhD\, Vice Chair of Research and Education\, Department of Computational Biomedicine\, Cedars-Sinai Medical Center
DESCRIPTION:TITLE: “ChatGPT for Clinical Informatics: what can LLMs do now for Health AI?” \nHosted by William Hsu for Medical Informatics \n 
URL:https://qcb.ucla.edu/event/frontiers-in-computational-biosciences-seminar-series-monday-december-4-2023-graciela-gonzalez-hernandez-phd-vice-chair-of-research-and-education-department-of-computational-biomedicine-cedars/
LOCATION:Boyer 159\, 611 Charles E. Young Dr. E.\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:QCBio Seminar Series
ATTACH;FMTTYPE=image/png:https://qcb.ucla.edu/wp-content/uploads/sites/14/2023/10/Picture6.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231207T120000
DTEND;TZID=America/Los_Angeles:20231207T143000
DTSTAMP:20231120T195105Z
CREATED:20231120T165356Z
LAST-MODIFIED:20231120T195105Z
UID:26038-1701950400-1701959400@qcb.ucla.edu
SUMMARY:Research-in-Progress (RIP) Seminar: MINI SYMPOSIUM - Emily Maciejewski (Ernst)\, Grad Student\, Computer Science - Chenlu Di (Lohmueller)\, Postdoc\, Ecology & Evolutionary Biology -  Qingyang Wang (Li JJ)\, Grad Student\, Statistics - Alex Bermudez (Lin)\, Grad Student\, Bioengineering
DESCRIPTION:12pm: Emily Maciejewski (Ernst)\, Grad Student\, Computer Science \nTITLE:  “Cross-species and tissue imputation of species-level DNA methylation samples” \nABSTRACT: DNA methylation data is highly informative to study a variety of aspects of mammalian biology. The availability of such data for many mammals at conserved sites was recently vastly enhanced by the development and large-scale application of the mammalian methylation array. For instance\, we consider here 13\,245 samples profiled on this array representing 348 species and 59 tissues from 746 species-tissue combinations. While having some coverage of many different species and tissue types\, this data only captures 3.6% of potential species-tissue combinations. We thus developed CMImpute (Cross-species Methylation Imputation) which uses a Conditional Variational Autoencoder to impute DNA methylation of non-profiled species-tissue combinations. In cross-validation\, we show that CMImpute yields high correlation with held-out observed values\, outperforming multiple baselines. We then train a model on all the data to impute 19\,786 new species-tissue combinations. We expect CMImpute and our imputed data resource will be useful for DNA methylation analyses across mammalian species. \n  \n12:30pm: Chenlu Di (Lohmueller)\, Postdoc\, Ecology & Evolutionary Biology \nTITLE: “Inference of fitness effects of mutations in noncoding regions of the human genome” \nABSTRACT: TBD \n  \n1:30pm: Qingyang Wang (Li JJ)\, Grad Student\, Statistics \nTITLE “Review of computational methods for estimating cell potency from single-cell RNA-seq data” \nABSTRACT: In single-cell RNA sequencing (scRNA-seq) data analysis\, a critical challenge is to infer hidden dynamic cellular processes from measured static cell snapshots. To tackle this challenge\, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring trajectories (or pseudotime) and RNA velocity\, another important perspective is to estimate the differentiation potential of cells\, which is commonly referred to as “cell potency.” In this review\, we provide a comprehensive summary of 11 computational methods that estimate cell potency from scRNA-seq data under different assumptions\, some of which are even conceptually contradictory. We divide these methods into three categories: mean-based\, entropy-based\, and correlation-based methods\, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories\, providing a high-level intuition of each method. Moreover\, we use a unified set of mathematical notations to detail the 11 methods’ methodologies and summarize their usage complexities\, including the number of ad-hoc parameters\, the number of required inputs\, and the existence of discrepancies between the method description in publications and the method implementation in software packages. Realizing the conceptual contradictions of existing methods and the difficulty of fair benchmarking without single-cell-level ground truths\, we conclude that accurate estimation of cell potency from scRNA-seq data remains an open challenge. \n  \n2:00pm: Alex Bermudez (Lin)\, Grad Student\, Bioengineering \nTITLE: “TCell Morphology Impacts Chromatin States During Crowding” \nABSTRACT: Variability is an inherent characteristic of all biological systems\, exemplified by the diverse shapes\, sizes\, and gene expression profiles of cells comprising tissues. Despite its ubiquity\, our understanding of how such a phenotypic heterogeneity plays a role in regulating cell biology remains incomplete. In this talk\, I will discuss how cell shape heterogeneity arises and its impacts on chromatin organization of each cell during epithelial crowding\, a canonical process where cells proliferate until a densely packed monolayer forms. Our findings suggest that cell morphological heterogeneity is not mere noise\, but a crucial factor driving chromatin state and gene expression\, directing tissue development and remodeling.
URL:https://qcb.ucla.edu/event/research-in-progress-rip-seminar-mini-symposium-emily-maciejewski-ernst-grad-student-computer-science-chenlu-di-lohmueller-postdoc-ecology-evolutionary-biology-qingyang-wang-l/
LOCATION:Boyer Hall 130
CATEGORIES:QCBio Seminar Series
ATTACH;FMTTYPE=application/pdf:https://qcb.ucla.edu/wp-content/uploads/sites/14/2023/11/Mini-Symposium-12723-1.pdf
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