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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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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220103T160000
DTEND;TZID=America/Los_Angeles:20220103T170000
DTSTAMP:20260517T200704
CREATED:20220103T172107Z
LAST-MODIFIED:20220103T174333Z
UID:20336-1641225600-1641229200@qcb.ucla.edu
SUMMARY:Bioinformatics/Human Genetics Seminar Series: Shantanu Joshi\, PhD\, Assistant Professor\, Neurology\, Brain Mapping Center\, UCLA
DESCRIPTION:TITLE: “Aligning Shape Data from Brain Imaging: applications to fMRI time series\, diffusion tractography.” \nHosted by Jason Ernst.
URL:https://qcb.ucla.edu/event/bioinformatics-human-genetics-seminar-series-shantanu-joshi-phd/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Bioinformatics Weekly Seminar,Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/01/1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220105T113000
DTEND;TZID=America/Los_Angeles:20220105T120000
DTSTAMP:20260517T200704
CREATED:20211207T185642Z
LAST-MODIFIED:20220106T165152Z
UID:20078-1641382200-1641384000@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Jee Yun Han (Boutros)\, Graduate Student in MBIDP (Molecular Biology Interdepartmental Doctoral Program)
DESCRIPTION:TITLE: “Comprehensive study of gene expression outliers and their regulation mechanisms in pan-cancer.” \nABSTRACT: With the ultimate aim of improving the management of cancer\, many groups have studied the molecular characteristics of tumors. Typically\, the heterogeneity of cancer evolves rapidly to adapt to its microenvironment\, helping cancer evade selective pressures and progress. These various subclones result in resistance to anti-cancer therapies. The extreme dysregulation of some gene such as oncogene is a prominent feature of cancer that can play a critical role in cancer tumorigenesis and accelerating cancer evolution by providing cancer cells with a selective growth advantage. Further research is needed to understand cancer heterogeneity and the extreme gene dysregulation\, and these studies will enable us to solve many limitations and obstacles for the inhibition and effective treatment of cancer. In previous studies\, cancer outliers such as BCR-ABL and TMPRSS2-ERG were identified as cancer drivers or drug targets\, and those showed strong associations with clinical outcomes. The gene expression outliers of cancer are likely to be caused by the diverse genetic and epigenetic variation frequently occurred in cancer. Despite of the important pathological function of cancer outliers\, to date\, the researches related to outliers have been only limited to a single type of cancer or a single gene\, which is insufficient for characterizing them and comprehending their pathological roles in cancer. To fill the gap of our understanding regarding cancer outliers\, this proposal will identify the outliers from various cancer types using our novel statistical method and explore the biological functions of outliers in individual cancer\, figuring out the unique feature of each cancer. With integrated analysis of the clinical outcome data and the features of outliers within and across cancer types\, it will show how outliers affect the progression of cancer. Comparative analysis transcriptome and proteome data analysis will address whether RNA outliers can be propagated into the protein level\, prioritizing candidates. Furthermore\, the molecular mechanisms for outlier generation in terms of genetic and epigenetic variation will be investigated in cancer\, enabling us to understand the central pathways associated with extremely abnormal gene expression in individual cancers. The proposed study is expected to deepen our understanding of the impact of outliers on different cancers by dissecting their dysregulation\, and\, in turn\, will allow us to identify a novel cancer driver and potential drug target. \n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Jee-Yun-Han-edited.mp4\n 
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-jee-yun-han-boutros-graduate-student-in-mbidp/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/png:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/12/JeeYun.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220105T120000
DTEND;TZID=America/Los_Angeles:20220105T123000
DTSTAMP:20260517T200704
CREATED:20211209T225609Z
LAST-MODIFIED:20220106T165920Z
UID:20130-1641384000-1641385800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Jianxiao Yang (Suchard)\, Grad Student in Biomathematics
DESCRIPTION:TITLE: “Massive parallelization of massive sample-size survival analysis.” \nABSTRACT: \nLarge-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However\, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this paper\, we use graphics processing units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically\, we develop and apply time andmemory efficient single-pass parallel scan algorithms for Cox proportional hazardsmodels and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach. We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders-of-magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Jianxiao-Yang-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-jianxiao-yang-suchard-grad-student-in-biomathematics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/png:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/12/Jianxiao-Yang.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220110T160000
DTEND;TZID=America/Los_Angeles:20220110T170000
DTSTAMP:20260517T200704
CREATED:20220103T172801Z
LAST-MODIFIED:20220103T174408Z
UID:20345-1641830400-1641834000@qcb.ucla.edu
SUMMARY:Bioinformatics/Human Genetics Seminar Series: Lauren McIntyre\, PhD\, Professor\, Molecular Genetics & Microbiology\, University of Florida
DESCRIPTION:TITLE: “Climate change and maize response to pollutants: gene content\, expression and regulation.” \nHosted by Kirk Lohmueller.
URL:https://qcb.ucla.edu/event/bioinformatics-human-genetics-seminar-series-lauren-mcintyre-phd-professor-molecular-genetics-microbiology-university-of-florida/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/01/2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220112T113000
DTEND;TZID=America/Los_Angeles:20220112T120000
DTSTAMP:20260517T200704
CREATED:20211209T201215Z
LAST-MODIFIED:20220118T163732Z
UID:20116-1641987000-1641988800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Evan Maltz (Wollman)\, Graduate Student in BMSB (Biochemistry\, Molecular and Structural Biology)
DESCRIPTION:TITLE: “Phenotypic consequences of gene expression variability.” \nABSTRACT: The central dogma of biology proposes that information flows from genes to RNA to protein\, determining protein identity and abundance. Single cell transcriptome measurements provide ample information about RNA abundance in cells. While the expression of individual genes clearly matters for determining phenotype\, they do not work independently. How the information in RNA abundances is combined to determine cellular phenotype remains unclear. Here we address this question for a single cellular phenotype\, the dynamic Ca2+ signaling response to a ligand. Using an information theory approach\, we estimated the mutual information between the expression of subsets of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found a high degree of dependency that corresponds to 60% of Ca2+ signal entropy\, yet most genes have redundant information. Furthermore\, a relatively small group of genes preserve most of the dependency. Our results demonstrate a high degree of regulatory elasticity with small changes in RNA abundance informing an emergent cellular phenotype. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Evan-Maltz-edited.mp4\n  \nVideos can also be viewed on the QCBio YouTube Channel
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-evan-maltz-wollman-graduate-student/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/png:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/12/Malt-Evan.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220112T123000
DTEND;TZID=America/Los_Angeles:20220112T130000
DTSTAMP:20260517T200704
CREATED:20211202T150300Z
LAST-MODIFIED:20220114T205155Z
UID:20052-1641990600-1641992400@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Dongyuan Song (Li JJ)\, Graduate Student in Bioinformatics
DESCRIPTION:TITLE: “scDesign3: an all-in-one statistical framework that generates realistic single-cell omics data and infers cell heterogeneity structure.” \nABSTRACT: The generation of realistic synthetic data is essential for benchmarking numerous computation tools developed for single-cell omics data. Here we propose an all-in-one statistical framework that generates single-cell omics data from various cell heterogeneity structures\, including discrete cell types\, continuous cell trajectories\, and spatial cell locations. Our framework uses a unified probabilistic model with accessible likelihood. This probabilistic formulation is advantageous in that it enables a straightforward discernment of the heterogeneity structure that best fits a single-cell omics dataset\, by leveraging the statistical model selection principle. \nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Dongyuan-Song-edited.mp4\n 
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-dongyuan-song-li-jj-graduate-student-in-bioinformatics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/12/DongyuanISMB2021.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220124T160000
DTEND;TZID=America/Los_Angeles:20220124T170000
DTSTAMP:20260517T200704
CREATED:20220103T173144Z
LAST-MODIFIED:20220118T170727Z
UID:20350-1643040000-1643043600@qcb.ucla.edu
SUMMARY:Bioinformatics/Human Genetics Seminar Series: Jennifer Wilson\, PhD\, Assistant Professor\, Bioengineering\, UCLA
DESCRIPTION:TITLE: “Deriving network parameters for understanding drug effects.” \nHosted by Jason Ernst.
URL:https://qcb.ucla.edu/event/bioinformatics-human-genetics-seminar-series-jennifer-wilson-phd-assistant-professor-bioengineering-ucla/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/01/3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220131T160000
DTEND;TZID=America/Los_Angeles:20220131T170000
DTSTAMP:20260517T200704
CREATED:20220103T173410Z
LAST-MODIFIED:20220118T170814Z
UID:20355-1643644800-1643648400@qcb.ucla.edu
SUMMARY:Bioinformatics/Human Genetics Seminar Series: Doc Edge\, PhD\, Assistant Professor\, Quantitative and Computational Biology\, USC
DESCRIPTION:TITLE: “The new forensic genetics: ‘long-range’ search and genetic privacy.” \nHosted by Nandita Garud.
URL:https://qcb.ucla.edu/event/bioinformatics-human-genetics-seminar-series-doc-edge-phd-assistant-professor-quantitative-and-computational-biology-usc/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2022/01/4.jpg
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