BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//The Global Plant Council - ECPv6.16.5//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://globalplantcouncil.org
X-WR-CALDESC:Events for The Global Plant Council
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Madrid
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20211031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20221030T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20231029T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20211031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20221030T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20231029T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20221205
DTEND;VALUE=DATE:20221208
DTSTAMP:20221123T082925Z
CREATED:20221123T082721Z
LAST-MODIFIED:20221123T082925Z
UID:17548-1670198400-1670457599@globalplantcouncil.org
SUMMARY:XV REUNIÓN DE BIOLOGÍA VEGETAL CHILENA
DESCRIPTION:More info here: https://biologiavegetal.cl/
URL:https://globalplantcouncil.org/event/xv-reunion-de-biologia-vegetal-chilena/
CATEGORIES:Plant Science
ATTACH;FMTTYPE=image/jpeg:https://globalplantcouncil.org/wp-content/uploads/2019/05/15442197_596437100480891_8624526746020855258_n-e1556906688607.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20221207T090000
DTEND;TZID=Europe/London:20221207T100000
DTSTAMP:20220907T163340Z
CREATED:20220907T163340Z
LAST-MODIFIED:20220907T163340Z
UID:17009-1670403600-1670407200@globalplantcouncil.org
SUMMARY:Machine learning in plant–pathogen interactions: empowering biological predictions from field scale to genome scale
DESCRIPTION:Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here\, Jana reviews application areas in plant–pathogen interactions that have recently benefited from ML. She provides an under-the-hood glance into her developed suite of ML-based tools for pathogen effector prediction such as EffectorP. Jana will discuss common pitfalls and challenges she encountered during the development of ML approaches. Finally\, she will highlight future opportunities for ML as a tool for dissecting plant–pathogen interactions\, for example through integration of AlphaFold predictions or ML-driven effector gene annotation. \n—— \nJana is a bioinformatician interested in the interactions between plants and their pathogens. Her work involves using computational techniques to gain new insights into plant–pathogen relationships\, and her contribution includes widely used software tools for fungal effector prediction using machine learning. She completed a M.Sc. in Computer Science with a focus on Bioinformatics at the University of Freiburg\, Germany. In 2008\, she commenced a Ph.D. at the University of Western Australia in which she developed novel methods for the computational prediction of non-coding RNA structure. In 2012\, she joined CSIRO as a Postdoctoral Fellow to work on plant-pathogen interactions. In 2018\, she joined the Australian National University as a Research Fellow at the Biological Data Science Institute and was awarded a 2019 ARC Discovery Early Career Researcher Award (DECRA) to uncover how rust fungi cause devastating plant diseases. In 2021 she joined CSIRO as a Senior Research Scientist where she leads the Data Intensive Approaches team. Jana’s Tansley insight ‘Machine learning in plant–pathogen interactions: empowering biological predictions from field scale to genome scale’\, focuses on machine learning (ML) applications in areas in plant–pathogen interactions and highlights benefits and challenges in using ML approaches.
URL:https://globalplantcouncil.org/event/machine-learning-in-plant-pathogen-interactions-empowering-biological-predictions-from-field-scale-to-genome-scale/
LOCATION:Online
CATEGORIES:Plant Science
ATTACH;FMTTYPE=image/png:https://globalplantcouncil.org/wp-content/uploads/2022/09/New-Phytologist-Now-Logo-with-strapline-transparent-background-e1662567585364.png
END:VEVENT
END:VCALENDAR