Dear all, We are pleased to announce the online Physalia course Machine Learning for Multi-Omics Integration, taking place from 21–23 September. Course website: [ https://www.physalia-courses.org/courses-workshops/multiomics/ ]( https://www.physalia-courses.org/courses-workshops/multiomics/ ) This course will introduce machine learning methodologies for integrating large-scale biological and biomedical data generated through Next-Generation Sequencing (NGS) and other Omics technologies. Through a combination of lectures and hands-on sessions, participants will explore supervised, unsupervised, deep learning, and single-cell Omics integration approaches. Participants are expected to have basic familiarity with the UNIX environment and beginner-level experience in R and/or Python. By the end of the course, participants will be able to: Understand machine learning approaches for biological data analysis Select appropriate tools for integrative Omics analysis Design and implement integrative bioinformatics workflows Apply suitable methodologies to specific biological research questions For the full list of our courses and workshops, please visit: [ https://www.physalia-courses.org/courses-workshops/ ]( https://www.physalia-courses.org/courses-workshops/ ) Best regards, Carlo
-------------------- Carlo Pecoraro, Ph.D Physalia-courses DIRECTOR [email protected] mobile: +49 17645230846 [ Bluesky ]( https://bsky.app/profile/physaliacourses.bsky.social ) [ Linkedin ]( https://www.linkedin.com/in/physalia-courses-a64418127/ ) [[alternative HTML version deleted]] _______________________________________________ R-sig-genetics mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-genetics
