HC@AIxIA: AI&Health Seminar Series (2024) - MARCH 8th

[apologize for multiple postings]

The THIRD seminar of the "AI & Health" series as hosted by HC@AIxIA, i.e.,
the "Artificial Intelligence for Healthcare" working group of the Italian
Association for Artificial Intelligence, is announced. *Save the date: 8
March.*

We hope you will attend and participate in the discussion on the relevant
topics that will be presented and by our speakers.
*Feel free to share this with those potentially interested.*
Please find some details below. All directions for participating are
available at https://aixia.it/gruppi/hc/.


*== Are you interested in Joining the group? ==*
Please head to https://aixia.it/en/gruppi/hc/ fo find out how. Do not
hesitate to contact us at hc-aixia@googlegroups.com for any information or
clarification.


Thank you for your interest in the AI & Health seminar series and the
HC@AIxIA working group, and see you soon!

Sincerely,
Francesco Calimeri, Mauro Dragoni, Fabio Stella
(coordinators of the HC@AIxIA working group)



*== March 2024 seminar ==*
*Link to participate*:
https://unimib.webex.com/unimib/j.php?MTID=m42fa53368d1f644d66bb75fbd173b7dc

*2024 MARCH 8 - 4:30PM CET*
*Andrea Palladino and Margherita Bodini*
GSK (Siena, Italy)

*Title*: Natural language processing and deep learning for genome
classification

*Abstract*: Machine learning classification of entire genome sequences
would find many important applications in the bacteriology field. Among the
most relevant, there are population genomics, antibiotic resistance
monitoring, and outbreak investigation. Despite the prosperous application
of AI to genetic sequences, especially in eukaryotes (Y. Ji et al.
Bioinformatics (2021), Z. Avsec et al., Nature Methods (2021)), it is still
challenging to use machine learning on the complete genome sequence of an
organism. In this seminar we will show various approaches to apply Natural
Language Processing (NLP) to biological sequences. We will guide attendants
in understanding the relevance and some details of such methods and show
practical examples of application to Neisseria meningitidis genome. We will
describe our recent work on the classification of B/non-B capsules and on
the identification of strains that colonize asymptomatically the
nasopharynx (carrier) from those that cause meningitis or sepsis (disease),
comparing with the state of the art methodology. Finally, we will show how
deep learning can be applied to the same scopes, with practical examples.

*Short Bio*: Andrea Palladino is a physicist by formation, working in the
field of artificial intelligence since 2020. He is senior data scientist at
GSK in the group of systems vaccinology, applying machine learning
algorithms to human data in the context of immuno-senescence, to
investigate the relationship between chronological age and "age” of the
immune system.
*Short Bio*: Margherita Bodini is a Bioinformatician, with PhD in
computational biology at the University of Milan. She works as senior data
scientist at GSK in the group of bacterial computational genomics, applying
genomic data analysis and machine learning towards research, development,
and life cycle management of vaccine products.