online course: AI FOR GENOMICS: FROM CNNS AND LSTMS TO TRANSFORMERS

[Physalia-courses]

Dear colleagues,



We’re excited to announce our upcoming online course: AI FOR GENOMICS: FROM CNNS AND LSTMS TO TRANSFORMERS







Dates: 9–11 September

Course website: https://www.physalia-courses.org/cou...-for-genomics/







This course offers a hands-on introduction to modern AI approaches in genomics, including CNNs, LSTMs, and Transformers. Participants will learn how to apply these powerful tools to sequence classification, functional element prediction, metagenomic source tracking, and more—using practical coding labs and interactive notebooks.







By the end of this course, participants will be able to:





• Understand and implement LSTM and CNN architectures for genomic sequence data

• Apply attention mechanisms to improve genomic feature extraction and prediction

• Train simple Transformer models for sequence classification or functional element prediction

• Use notebooks to run and modify ML & DL workflows for genomics research

• Interpret model outputs and assess performance using biological context







For the full list of our courses and workshops, please visit: https://www.physalia-courses.org/courses-workshops/







Best regards,



Carlo











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Carlo Pecoraro, Ph.D





Physalia-courses DIRECTOR



[email protected]

[Davidjohn321]

This looks like a fascinating course, Carlo thanks for sharing! The blend of CNNs, LSTMs, and Transformers in genomics sounds like a great way to bridge AI concepts with biological data analysis.

It’s interesting how the concept of Transformers in AI parallels some principles in electrical systems too like Control Transformers, which help manage and stabilize power flow. In both cases, there’s that element of “translating” or “regulating” complex input into something usable and efficient.

Looking forward to exploring more about how attention mechanisms can enhance genomic feature prediction.