
This record includes training materials associated with the Australian BioCommons webinar 'Future of AI in Life Sciences'. This webinar took place on 29 April 2026. Webinar Description Artificial intelligence (AI) and machine learning (ML) are rapidly changing the way we work in the life sciences. Recently, Australian BioCommons called upon the community to share how they apply these technologies to molecular data analysis to help map future needs. Join us for this dedicated webinar where we will share a summary of the findings from our national consultation. This session will explore the general survey results, highlighting the national trends, bottlenecks, and digital infrastructure gaps you've helped identify. It will also include a ‘first look’ at our initial training program, giving you a sneak peek into the workshops and resources we’ll be launching later this year to better support the application of AI in life science research. Speakers: Dr Minh Huynh, AI in Research Training Lead, Australian BioCommons and Sydney Informatics Hub; Dr Benjamin Goudey, AI Technical Lead, Australian BioCommons Host: Melissa Burke, Australian BioCommons Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Future of AI in Life Sciences Zenodo - Event metadata - webinars.pdf: Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Future of AI in Life Sciences Webinar.pdf: slides presented during the webinar. Files and materials shared elsewhere: Recording of the presentation on the Australian BioCommons YouTube channel: https://youtu.be/yPBuHT1zagI
Machine Learning, Artificial Intelligence, Life Sciences
Machine Learning, Artificial Intelligence, Life Sciences
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
