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BEGIN:VEVENT

CATEGORIES:Utilities
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20220715T191028Z
DESCRIPTION:Despite the recent success of AI-based strategies for protein 
 structure prediction\, single-particle cryo-electron microscopy (EM) is s
 till  considered a gold-standard technique for determining three-dimensio
 nal structure of proteins at resolutions that are sufficient to visualize
  molecular level interactions. Determining a structure with this method\,
  however\, requires collecting images for several days and subjecting the
 m to lengthy data processing protocols that can take weeks to complete.  
 Moreover\, a single biomedical study typically requires the determination
  of multiple protein structures\, only adding to the logistical\, resourc
 e and computational complexity of single-particle cryo-EM projects. In th
 is talk\, I will describe our efforts to employ AI-based techniques for f
 eature recognition to drive the development of strategies to accelerate s
 tructure determination using cryo-EM. For example\, finding the condition
 s to stabilize a macromolecular target for imaging remains the most criti
 cal barrier to determining its structure. SmartScope is the first framewo
 rk to streamline\, standardize\, and automate specimen evaluation using d
 eep-learning-based object detection\, allowing it to perform specimen scr
 eening in a fully automated manner. During the downstream data analysis\,
  randomly distributed copies of the protein of  interest need to be ident
 ified\, extracted and averaged in 3D to obtain a high-resolution structur
 e. Existing neural-network-based detection algorithms require extensive l
 abeling and are very slow to train. Leveraging positive unlabeled learnin
 g and consistency regularization\, we propose a novel framework that is a
 ble to identify particles much faster than previously possible while stil
 l using very few labels. Altogether\, these advances in AI-driven cryo-EM
  greatly facilitate and accelerate the structural analysis of important b
 iomedical targets\, thus lowering the barrier of adoption of this powerfu
 l technique for protein structure determination.\n\nThis session is a par
 t of the monthly seminar series organized by SPARK: AI Health Initiative 
 for Medical Imaging. The seminar will highlight outstanding work in medic
 al imaging at Duke and beyond. The seminar recordings will be publicly av
 ailable.\n\nThe SPARK initiative focuses on development\, validation\, an
 d clinical implementation of artificial intelligence algorithms for broad
 ly understood medical imaging by bringing together the technical and clin
 ical expertise across Duke campus.
DURATION:PT1H
DTSTAMP:20220719T133648Z
DTSTART;TZID=America/New_York:20220802T120000
LAST-MODIFIED:20220719T133648Z
LOCATION;X-BEDEWORK-UID=2c918085-71ed832f-0172-098647fa-0000767f:Virtual
STATUS:CONFIRMED
SUMMARY:AI Health Spark Seminar Series: Entering the Era of AI-driven Cryo
 -EM with SmartScope
UID:CAL-8a039360-81409ef1-0182-0344fd01-0000014ademobedework@mysite.edu
URL:https://duke.zoom.us/webinar/register/WN_DfnvOfNPQKqzT9WEfS-G0w
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
 ties/Main
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=aihealth@dm.duke.edu:Duk
 e AI Health
X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp_provost
 _plusDataScience,/principals/users/agrp_SchoolofMedicine_Biostatisticsand
 Bioinformatics,/principals/users/agrp_ArtsandSciences_ComputerScience,/pr
 incipals/users/agrp_SOM_ClinDep_Radiology,/principals/users/agrp_PrattSch
 ool_ECE,/principals/users/agrp_InformationInitiativeatDuke,/principals/us
 ers/agrp_PrattSchool,":+DataScience (+DS)\,Biostatistics and Bioinformati
 cs\,Computer Science\,Department of Radiology\,Electrical and Computer En
 gineering (ECE)\,Information Initiative at Duke (iiD)\,Pratt School of En
 gineering
X-BEDEWORK-SPEAKER:Presented by: Alberto Bartesaghi\, Associate Professor 
 of Computer Science\, Duke University with host Maciej Mazurowski\, PhD\;
  Associate Professor in Radiology\, Duke University
X-BEDEWORK-SUBMITTEDBY:tmt26 for AI Health (agrp_SOM_Forge)
END:VEVENT
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