Join us for an evening of fast-paced science, great conversations, and free beer (or a soft drink). Expect three 10-minute talks from young researchers working at the forefront of bioinformatics and AI in life sciences, followed by informal networking with beers!
Registration can be done below!
Exploring the limitations of mass spectrometry in confidently identifying peptides
Charlotte holds a master’s degree in Biochemistry and Biotechnology and completed her PhD in Computational Mass Spectrometry. In her current postdoctoral research, she focuses on scientific integrity in mass spectrometry, including the method’s limitations and privacy-related challenges.
About the talk: Two distinct peptides can result in nearly identical spectra, making them indistinguishable even though they may have very different biological implications. Prediction models were used as a stand-in for experimental data to investigate these ambiguities in a controlled setting and to highlight the boundaries of current analytical methods.
How Gene Networks Reveal the Biology of Acute Myeloid Leukemia Patients
I started my journey into bioinformatics in 2022, and now I am doing a PhD on a cancer research project. Outside of work I am a big fan of football and hip hop music.
AML patients have very different forms of the disease, even when given the same diagnosis. Using single-cell analysis, I mapped how genes interact inside cells and found patient-specific patterns that could help classify patients and serve as a foundation for the development of personalized treatments.
Machine learning reveals substantial autosomal variation in recombination landscapes between two felid lineages.
I am Ward Hendrix, a master’s student in Evolutionary and Behavioural Biology at the University of Antwerp with a strong interest in evolutionary genomics. I am currently completing my thesis on the recombination landscape of the clouded tiger cat in the Svardal Lab under the supervision of Prof. Hannes Svardal and Dr. Jonas Lescroart.
In this talk, I will present the genomic recombination landscapes inferred for the clouded tiger cat (Leopardus pardinoides) using a recurrent neural networks (RNN) approach with ReLERNN. Comparison with the domestic cat (Felis catus) reveals substantial variation in autosomal recombination rates within felids. I will show how recombination rate correlates with key genomic features and how recombination-aware phylogenomic approaches can leverage these maps to better resolve the evolutionary history of the Leopardus-lineage.
