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X-WR-CALNAME:Faculty of Science and Technology | University of Macau
X-ORIGINAL-URL:https://www.fst.um.edu.mo
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DTSTART:20170101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170111T103000
DTEND;TZID=Asia/Macau:20170111T113000
DTSTAMP:20260514T143514
CREATED:20170111T023012Z
LAST-MODIFIED:20220927T045329Z
UID:6006-1484130600-1484134200@www.fst.um.edu.mo
SUMMARY:Modeling transcription factor binding affinity landscape through machine learning
DESCRIPTION:Instructors/Speakers\nProf. Xin GAO\nAssociate Professor of Computer Science\, Computational Bioscience Research Center (CBRC)\nKing Abdullah University of Science and Technology (KAUST)\nSaudi Arabia \nAbstract\nTranscription factors (TF) are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. An accurate characterization of TF-DNA binding affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. In this talk\, I will introduce two machine learning methods that we recently developed for modeling TF-DNA binding affinity. The first method is a two-round support vector regression with weighted degree kernel\, which can accurately capture important k-mers that contribute to high and low affinity values. In contrast\, the second method aims at incorporating both position-specific information and long-range interaction. It is an end-to-end learning framework that combines the strength of graphical models\, Hilbert space embedding\, and deep learning. \nBiography\nDr. Xin Gao is an associate professor of computer science in the Computer\, Electrical and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST)\, Saudi Arabia. He is also a PI in the Computational Bioscience Research Center at KAUST and an adjunct faculty member at David R. Cheriton School of Computer Science at University of Waterloo\, Canada. Prior to joining KAUST\, he was a Lane Fellow at Lane Center for Computational Biology in School of Computer Science at Carnegie Mellon University\, U.S.. He earned his bachelor degree in Computer Science in 2004 from Computer Science and Technology Department at Tsinghua University\, China\, and his Ph.D. degree in Computer Science in 2009 from David R. Cheriton School of Computer Science at University of Waterloo\, Canada. \nDr. Gao’s research interests are building computational models\, developing machine learning techniques\, and designing efficient and effective algorithms\, with particular focus on applications to key open problems in structural biology\, systems biology and synthetic biology. He has co-authored more than 100 research articles in the fields of bioinformatics and machine learning. \n 
URL:https://www.fst.um.edu.mo/event/modeling-transcription-factor-binding-affinity-landscape-through-machine-learning/
LOCATION:E11-1012
CATEGORIES:event_list,seminarslectures
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BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20170111T143000
DTEND;TZID=Asia/Macau:20170111T153000
DTSTAMP:20260514T143514
CREATED:20170111T063022Z
LAST-MODIFIED:20220927T045329Z
UID:6011-1484145000-1484148600@www.fst.um.edu.mo
SUMMARY:The rare event study and its applications in the biological jump processes
DESCRIPTION:Instructors/Speakers\nProf. Tiejun LI\nProfessor\nSchool of Mathematical Sciences\nPeking University\nChina \nAbstract\nThe construction of energy landscape for bio-dynamics is attracting more and more attention recent years. In this talk\, I will introduce the strategy to construct the landscape from its connection with rare events\, which relies on the large deviation theory for Gillespie-type jump dynamics. In the application to a typical genetic switching model\, the two-scale large deviation theory is developed to take into account the fast switching of DNA states. The comparison with other proposals are also discussed. We demonstrate different diffusive limits arise when considering different regimes for genetic translation and switching processes. I will also talk about its applications in understanding the S-phase checkpoint activation mechanism for budding yeast. This is a joint work with Fangting Li\, Xianggang Li\, Cheng Lv and Peijie Zhou. \nBiography\nProf. Li obtained his bachelor degree and PhD at Tsinghua University and is now a full professor at Peking University\, his basic interest is the stochastic modeling and simulations in Science such as chemical reaction kinetics\, rare events\, Anderson localization\, multiscale modeling of complex fluids\, statistical data analysis and so on. Prof. Li has published more than 40 papers on PNAS\, J. Chem. Phys.\, Comm. Math. Phys. and so on. \n 
URL:https://www.fst.um.edu.mo/event/the-rare-event-study-and-its-applications-in-the-biological-jump-processes/
LOCATION:E11-1038
CATEGORIES:event_list,seminarslectures
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