This course explores the application of modern AI architectures—Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers—to genomic and metagenomic data. Students will gain practical experience through hands-on coding labs and interactive notebooks, learning how to model sequence data, extract biologically meaningful features, and interpret results. Emphasis is placed on real-world applications, including prediction of genomic functional elements, sequence classification and source tracking, as well as biological sequence generation.

•    Basic knowledge of molecular biology and genomics (e.g., genetic variation)
•    Familiarity with Python/R programming

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

Day 1:14:00–20:00 (Berlin time)

• Introduction to artificial neural networks and concepts of convolution and recurrence.
• Encoding genomics data for CNNs and LSTMs in Keras and TensorFlow.

Day 2: 14:00–20:00 (Berlin time)

• Training CNNs and LSTMs for sequence classification and functional element prediction.

• Applying NLP concepts to genomics data: bag-of-words and Word2Vec models.

   

Day 3: 14:00–20:00 (Berlin time)

• Metagenomic source tracking with CNNs: microbial community sequence annotation.

• Biological sequence generation with attention, transformer model for genomics applications.

   

Should you have any further questions, please send an email to info@physalia-courses.org