Pro-/Seminar: Machine learning tools for genomics and proteomics

MAXIMUM NUMBER OF PARTICIPANTS
16

Mandatory

• Registration by e-mail to Kerstin Gronow-Pudelek by April 10, 2025
• Presence at the preliminary meeting on April 15, 2025
• Registration in the LSF by May 6, 2025, is mandatory; later registration is not possible.

If there are more than 16 registrations, the lottery will decide.

Requirements

Knowledge corresponding to semester 4 (proseminar) or knowledge corresponding to BSc degree (seminar)

Conditions for Certification

• Successful presentation
• Regular participation (at least 75% of talks)
• Submission of a two-page report by e-mail to the tutor by tba

Preliminary Meeting

• Purpose: placement of the topics and organization
• Time and Place: Tuesday, April 15, 2025, 12:30, E2 1, room 007

Schedule

Additional Information

Please regard the important hints for your seminar presentation!

Topics

1. PPI-affinity: A web tool for the prediction and optimization of protein–peptide and protein–protein binding affinity (Proseminar)
2. TPepPro: a deep learning model for predicting peptide–protein interactions (Seminar)
3. Learning spatial structures of proteins improves protein–protein interaction prediction (Seminar)
4. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning (Seminar)
5. Scalable emulation of protein equilibrium ensembles with generative deep learning (Seminar)
6. Transfer learning for cross-context prediction of protein expression from 5’UTR sequence (Seminar)
7. TIS Transformer: remapping the human proteome using deep learning (Proseminar, Seminar)
8. Effective gene expression prediction from sequence by integrating long-range interactions (Seminar)
9. Building a knowledge graph to enable precision medicine (Proseminar, Seminar)
10. Graph-BERT and language model-based framework for protein–protein interaction identification (Seminar)
11. Context-aware transcript quantification from long-read RNA-seq data with Bambu (Seminar)
12. SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification (Proseminar, Seminar)
13. DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning (Seminar)
14. DeepEdit: single-molecule detection and phasing of A-to-I RNA editing events using nanopore direct RNA sequencing (Proseminar, Seminar)
15. m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data (Seminar)
16. dsRID: in silico identification of dsRNA regions using long-read RNA-seq data (Proseminar, Seminar)