Forschungsseminar der Arbeitsgruppe Data Integration in the Life Sciences (DILiS). Auch offen für Seminarteilnahmen im Masterstudium, Online-Teilnahme möglich. Bitte entnehmen Sie Termine dem aktuellen Plan im Whiteboard!
Wir starten um 12:00 s.t.
Einwahl für das hybride Setting (neu ab 6.11.): https://fu-berlin.webex.com/fu-berlin/j.php?MTID=m3c1cf6f19adf01836a392a2bdc787d30
Seminarraum vor Ort: T9 Raum 137
Das Seminar bietet Raum für die Diskussion weiterführender und integrativer Datenanalysetechniken, insbesondere Vorträge und Diskussion von laufenden oder geplanten Forschungsprojekten, Neuigkeiten von Konferenzen, Besprechung aktueller Literatur und Diskussion möglicher zukünftiger Lehrformate und -inhalte, und Vorstellungen, sowie Abschlussvorträge zu Abschlussarbeiten oder Projektseminaren. Die Seminarsprache ist weitestgehend Englisch. Gern können interessierte Studierende teilnehmen und unverbindlich vorbeischauen oder ein selbst gewähltes Thema von Interesse für die Arbeitsgruppe vorstellen. Achtung: Einzelne Termine können ausfallen oder verschoben werden. Kontaktieren Sie mich gern für Fragen (katharina.baum@fu-berlin.de)!
Agenda
| Date | Speaker | Topic |
|---|---|---|
| 04.10. | - | - |
| 11.10. | - | - |
| 18.10. | Katharina Baum & DILiS | Introduction and guidelines on participating in the seminar |
| 25.10. | DILiS | Journal Club: "Modeling polypharmacy side effects with graph convolutional networks", https://doi.org/10.1093/bioinformatics/bty294 |
| 01.11. | Pauline Hiort | ML-based drug combination prediction with graph-based features |
| 08.11. | Abhinav Mishra (30+10) | Mathematical Modeling of signaling networks in triple negative breast cancer cell lines |
| 15.11. | Swetha Rose Sebastian (15+10) Pascal Iversen (20+10) |
MSc Bioinformatics Thesis Introduction Machine learning for response prediction
|
| 22.11. | Thea Steuerwald (15+15) Jonah Reiner (15+15) |
BSc Bioinformatics Kolloquia: Multi-omics Analysis of Pulmonary Arterial Hypertension Data Drug Response Prediction in Cancer Cell Lines: Ablation Study and Input Modality Analysis of a Graph-Informed Deep Neural Network Model using Permutations |
| 29.11. | Emanuel Kemmler | Machine learning-based predictions of patient reactions in a peanut oral food challenge |
| 6.12. | Lilly Wiesmann (15+15) Flavio Morelli (45+15) |
BSc Bioinfo Kolloquium: Evaluation of different protein interaction networks and their versions regarding drug combination response motifs |
| 13.12. | Wictoria Krawczyk (45 + 15) | GNNs for drug synergy prediction |
| 20.12. | cancelled - merry Xmas! | |
| 27.12. | no lectures | |
| 3.1. | no lectures | |
| 10.1. |
Gia Cuong Pham (30 + 15) Ya-Ting Yang (10+5) |
The role of Cdr1as in neuronal gene regulation using molecular cartography BSc CS thesis topic intro: Drug-drug GNN for drug combination prediction and explanations |
| 17.1. | Oussama Bouanani (45 +15) | Scalable, non-linear and interpretable representations of genomic data |
| 24.1. | Gesa Röefzad (15+15) Katharina Baum |
BSc Bioinfo Thesis colloquium Guidelines for Reports, Checklist for Peer Feedback |
| 31.1. | Cancelled | Cancelled |
| 7.2. | Umur Can Kaya (45+15) | TBD |
| 14.2. | ||
| 21.2. | DILiS Team [internal] | Exam preparation |
| 28.2. | DILiS Team | Website Hackathon, conference planning, outreach |
Mögliche Vortragsthemen - Literaturliste
- [covered by Wictoria] Besharatifard, M., Vafaee, F. A review on graph neural networks for predicting synergistic drug combinations. Artif Intell Rev 57, 49 (2024). https://doi.org/10.1007/s10462-023-10669-z
- Heinken, A., Hertel, J., Acharya, G. et al. Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine. Nat Biotechnol 41, 1320–1331 (2023). https://doi.org/10.1038/s41587-022-01628-0
- Azeloglu EU, Iyengar R. Signaling networks: information flow, computation, and decision making. Cold Spring Harb Perspect Biol. 2015 Apr 1;7(4):a005934. doi: 10.1101/cshperspect.a005934.
- Trang Nguyen, Anthony Campbell, Ankit Kumar, Edwin Amponsah, Madalina Fiterau, Leili Shahriyari, Optimal fusion of genotype and drug embeddings in predicting cancer drug response, Briefings in Bioinformatics, Volume 25, Issue 3, May 2024, bbae227, https://doi.org/10.1093/bib/bbae227
- Halil Ibrahim Kuru, A Ercument Cicek, Oznur Tastan, From cell lines to cancer patients: personalized drug synergy prediction, Bioinformatics, Volume 40, Issue 5, May 2024, btae134, https://doi.org/10.1093/bioinformatics/btae134
- Sharon E Davis, Michael E Matheny, Suresh Balu, Mark P Sendak, A framework for understanding label leakage in machine learning for health care, Journal of the American Medical Informatics Association, Volume 31, Issue 1, January 2024, Pages 274–280, https://doi.org/10.1093/jamia/ocad178
- Li et al., Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets; Journal of Biomedical Informatics, Volume 152, 2024, https://doi.org/10.1016/j.jbi.2024.104621
- DysRegNet: Patient-specific and confounder-aware dysregulated network inference;
- Hao, M., Gong, J., Zeng, X. et al. Large-scale foundation model on single-cell transcriptomics. Nat Methods 21, 1481–1491 (2024). https://doi.org/10.1038/s41592-024-02305-7