====== 392232  Advanced Artificial Intelligence in Biomedicine ======
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| [[https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=569566499 | 392232]] | Schönhuth/Schlüter | Winter 2025 | Thu 12-14 in Y-1-202 (S) and Thu 10-12 in U2-200 (Ü) |

==== Contact ====

  * Prof. Dr. Alexander Schönhuth: [[mailto:aschoen@cebitec.uni-bielefeld.de|aschoen@cebitec.uni-bielefeld.de]]
  * Johannes Schlüter: [[mailto:j.schlueter@uni-bielefeld.de|j.schlueter@uni-bielefeld.de]]


==== Structure ====

  * Individual presentations (S): present contents of chosen scientific paper for 30 minutes, followed by 15 minutes for discussion
  * Technical report (S): 10-12 pages long
  * Paper journal (Ü): collection of summaries of at least half of the presentations; each summary should be at least 300 words


==== Papers ====

Please, see on [[https://drive.google.com/file/d/1hB_u0jPfM6RtLs1QmwAWkLKeqd-XAzZw/view?usp=sharing | Google Drive]]. The file is password protected. \\
If there is any problem with accessing a paper, write an email to [[mailto:lpianesi@cebitec.uni-bielefeld.de|Luna Pianesi]].

====Introduction====
{{teaching:2025winter:advanced_artificial_intelligence_in_biomedicine_introduction_default.mp4}} 
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{{teaching:2025winter:introduction-161025.pdf}}

====How to present====
{{teaching:2025winter:howtopresent.mp4}}
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{{teaching:2025winter:howtopresent.pdf}}

====How to write reports====
{{teaching:2025winter:aaibm_howtowritereports.mp4}}
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{{teaching:2025winter:howtowritereports.pdf}}
==== Timetable seminar sessions ====

| **Date** | **Topic** |
|16.10.2025 | Introduction, How to present |
|23.10.2025 |  -  |
|30.10.2025 |  -  |
|06.11.2025 |  -  |
|13.11.2025 |  -  |
|20.11.2025 | How to write reports, Presentations start \\ **Presentation**: Gregor Foitzik. "Protein Structure Generation Via Folding Diffusion" |
|27.11.2025 | **Presentation**: Sanket Veerkar. "Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures" \\ **Presentation**: Özay Öztürk. "Mamba: Linear-time Sequence Modelling with Selective State Spaces" |
|04.12.2025 | **Presentation**: Karthikeya Barla. "Quantum Mixed-State Self-Attention Network" \\ **Presentation**: Christopher Gerz. "Protein Docking Model Evaluation by Graph Neural Networks" |
|11.12.2025 | **Presentation**: Jonas Ginster. "Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition" \\ **Presentation**: Dustin Schulz. "Using GNN property predictors as molecule generators" |
|18.12.2025 | **Presentation**: Dana Meyer. "Multiscale transformers and multi-attention mechanism networks for pathological nuclei segmentation" \\ **Presentation**: Felix Duldinger. "Context-aware geometric deep learning for protein sequence design" |
|25.12.2025 | Holidays |
|01.01.2026 | Holidays |
|08.01.2026 | **Presentation**: Lokesh Mangamuri. "Combining Graph Attention and Recurrent Neural Networks in a Variational Autoencoder for Molecular Representation Learning and Drug Design" \\ **Presentation**: Genna Strathmann. "MEGANet: Multi-scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation" |
|15.01.2026 | **Presentation**: Anjali Anjali. "Biomedical literature mining: graph kernel-based learning for gene–gene interaction extraction" \\ **Presentation**: Mathis Wülpern. "Hypergraph Factorization for Multi-tissue Gene Expression Imputation" |
|22.01.2026 | **Presentation**: Abdulrahman Alzahabi. "DrugDAGT: a Dual-Attention Graph Transformer for Drug-Drug Interaction Prediction" \\ **Presentation**: Abu Emran Emon. "Graph-to-Sequence Retrosynthesis" |
|29.01.2026| **Presentation**: Jefferson Vormann. "gRNAde: Geometric Deep Learning for 3D RNA Inverse Design" \\ **Presentation**: Michael Bucks. "Molecule Edit Graph Attention Network: Modelling Chemical Reactions as Sequences of Graph Edits"  |
|05.02.2026| **Presentation**: Aya Benzine. "Contextual AI Models for Single-cell Protein Biology" \\ **Presentation**: Ergys Kosita. "Prospective De Novo Drug Design with Deep Interactome Learning"  |
|12.02.2026| **Presentation**: Kareem Elbanna. "TAnet: A New Temporal Attention Network for EEG-based Auditory Spatial Attention Decoding with a Short Decision Window" \\ **Presentation**: Khaled Saber. "EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction" \\ **Presentation**: Shambavi Arcot Velumani. "On the Scalability of GNNs for Molecular Graphs"  |