====== Advanced Artificial Intelligence in Biomedicine ======
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| [[https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=479123543|392232]]/[[https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=478739606| 392240]]  | Schönhuth, Knop  | Winter 2024/25 | Thu 12:00-14:00 X-E0-204 (S) and Thu 10:00-12:00 X-E0-204 (Ü) |

:!: The exercises will start subsequent to the presentations, probably in JAN/FEB 2025

==== Introduction / How to present ====
{{teaching:2024winter:aaibm:advanced_ai_in_biomedicine_default.mp4}} 


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{{teaching:2024winter:aaibm:howtopresent.pdf|introduction-101024.pdf}} \\
{{teaching:2024winter:aaibm:howtopresent.pdf|howtopresent.pdf}} 


==== How to write reports ====
{{teaching:2024winter:aaibm:howtowritereports.pdf|how to write reports.pdf}} 

==== Papers - more follow soon ====

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| Date | Student | **Title** | **Authors** | **Year** | **Journal** | 
| | Luca Schmidt  | Accurate structure prediction of biomolecular interactions with AlphaFold 3 | Josh Abramson et al. | 2024 | nature |
| | Lyon Brown| [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/drugdagt.pdf|DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction]] | Yaojia Chen et al. | 2024 | BMC Biology |
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/mlsnet.pdf|MLSNet: a deep learning model for predicting transcription factor binding sites]] | YuchuanZhang et al. | 2024 | Briefings in Bioinformatics |
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/mslocpred.pdf|MSlocPRED: deep transfer learning-based identification of multi-label mRNA sub cellular localisation]] | YunZuo et al. | 2024 | Briefings in Bioinformatics |
| | Angelica Gutu | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/scdfn.pdf|scDFN: enhancing single-cell RNA-seq clustering with Deep Fusion networks]] | Tianxiang Liu et al.| 2024 | Briefings in Bioinformatics |
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/deeppgd.pdf|DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism]] | Shoryu Teragawa et al. | 2024 | International Journal of Molecular Sciences |
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/biomedical_literature_mining.pdf|Biomedical literature mining: graph kernel-based learning for gene–gene interaction extraction]] | Ai-Ru Hsieh et al. | 2024 | European Journal of Medical Research |
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/sequence_modeling_and_design_from_molecular_to_genome_scale_with_evo.pdf|Sequence modeling and design from molecular to genome scale with Evo]] | Eric Nguyen et al. | 2024 | biorxiv|
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/caduceus.pdf|Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling]]  |Yair Schiff et al. | 2024 | arXiv |
| | |[[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/flow_matching_for_generative_modeling.pdf|Flow Matching for Generative Modeling]] | Yaron Lipman et al. | 2023 | arXiv  |
| | Fatih Altundas |[[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/learning_single-cell_perturbation_responses_using_neural_optimal_transport.pdf|Learning single-cell perturbation responses using neural optimal transport]]  | Charlotte Bunne et al. | 2023 | nature |
| | Philip Frese |[[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/privacy-preserving_decentralized_learning_methods_for_biomedical_applications.pdf|Privacy-preserving decentralized learning methods for biomedical applications]] |Mohammad Tajabadi et al. | 2024 | ScienceDirect |
| | |[[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/integrate_any_omics.pdf|Integrate Any Omics: Towards genome-wide data integration for patient stratification]] | Shihao Ma | 2024 | arXiv |
| 28.11.2024 | Racha Bekhouche |[[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/deeptta.pdf|DeepTTA: a transformer-based model for predicting cancer drug response]] |Likun Jiang et al. | 2022 |Briefings in Bioinformatics |
| | Henrik Folz |[[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2425/discovery_of_drug_omics_associations_in_type_2_diabetes_with_generative_deep-learning_models.pdf|Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models]] |Rosa Lundbye Allesøe et al. | 2023 |Nature Biotechnology|
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2024/se_3_-transformers-_3d_roto-translation_equivariant_attention_networks.pdf |SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks]]  | Fabian B. Fuchs et al. | 2020 | 34th Conference on Neural Information Processing Systems (NeurIPS 2020)| 
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2024/diffdock-_diffusion_steps_twists_and_turns_for_molecular_docking.pdf |DIFFDOCK: DIFFUSION STEPS, TWISTS, AND TURNS FOR MOLECULAR DOCKING]] | Gabriele Corso et al. | 2023| ICLR |
| 21.11.2024 | Fabian Molls | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2024/equibind-_geometric_deep_learning_for_drug_binding_structure_prediction.pdf |EQUIBIND: Geometric Deep Learning for Drug Binding Structure Prediction]] | HannesStärk et al. | 2022 | 39th International Conference on Machine Learning |
| | Felix Dyck | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2024/hyenadna-_long-range_genomic_sequence_modeling_at_single_nucleotide_resolution.pdf |HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution]] | Eric Nguyen et al. | 2023 | NeurIPS 2023 | 
| | Alexander Hüdepohl | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2024/missing_values_and_imputation_in_healthcare_data-_can_interpretable_machine_learning_help_.pdf |Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?]]  | Zhi Chen et al. | 2023 | Conference on Health, Inference, and Learning (CHIL) | 
| | | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2024/explainable_automated_coding_of_clinical_notes_using_hierarchical_label-wise_attention_networks_and_label_embedding_initialisation.pdf |Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation]] | Hang Dong et al. | 2021 |  Journal of Biomedical Informatics| 
| 21.11.2024 | Bhautik Lukhi | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2024/biogpt-_generative_pre-trained_transformer_for_biomedical_text_generation_and_mining.pdf |BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining]] | Renqian Luo et al. | 2022 | Briefings in Bioinformatics | 
| 12.12.2024 | Ismail Ceyhan | [[https://gds.techfak.uni-bielefeld.de/_media/teaching/literature/2024/antigen-specific_antibody_design_and_optimization_with_diffusion-based_generative_models_for_protein_structures.pdf |Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures]] | Shitong Luo et al. | 2022 | NeurIPS| 

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