Approaches to biomedical knowledge
Ontologies and Large Language Models
This course is held as a part of the Master Curriculum in Bioinformatics of the Free University Berlin.
This course enables students to master two essential frameworks for managing human knowledge in bioinformatics: Ontologies and Language Models
Lecture Schedule
- What to expect from this course
- Lecture 1: Ontologies: OBO, RDF, RDFS, and OWL
- Lecture 2: Gene Ontology (GO) and Overrepresentation Analysis
- Lecture 3: Bayesian algorithms for GO: Model-based gene-set analysis
- Lecture 4: Human Phenotype Ontology: Semantic similarity algorithms
- Lecture 5: From Logistic Regression to Neural Foundations
- Lecture 6: Neural networks
- Lecture 7: RNNs and LSTM (to do)
- Lecture 8: Embeddings and Word2Vec (partially finisheed)
- Lecture 9: Encoders
- Lecture 10: Decoder architecture (to do) *. Lecture 11: BERT and Sentence BERT (to do)
- Lecture 12: Reinforcement learning and LLMs (to do)
- Lecture 13: RAG, Fine tuning, Structured Outputs (to do)
- Lecture 14: Applications of LLMs in Medicine: Problems and opportunities (Short lecture followed by review period)
Homework
- Each lecture will be accompanied by a practical session to go over background material and review the homework.