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DTSTART;TZID=America/Los_Angeles:20220105T113000
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DTSTAMP:20260517T210117
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
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DTSTART;TZID=America/Los_Angeles:20220105T120000
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DTSTAMP:20260517T210117
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
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