With 5400 genes and several stages of development, it’s hard to find the right target for a #malaria vaccine 🧬 Learn about the surprising benefit of transmission-blocking vaccines, which could be part of the solution, with @iliciub1 @heqixinAA @giocarpi pod.fo/e/26f4b5

I'm very excited to share our latest publication in @EBioMedicine @TheLancet! This review delves into the crucial role of #TAMReceptors—Tyro3, AXL & MerTK—in PDAC #PancreaticCancer, one of the deadliest cancers. #CancerResearch @PNavarro_Lab 🧵👇 sciencedirect.com/science/ar…

To preprocess or not to preprocess your #EEG (when building #ML models) 🔮? 💫We are thrilled to share our latest #preprint, studying the challenge of learning brain-specific biomarkers from EEG 🧠📶 using #ML ⚙️🖥️: biorxiv.org/content/10.1101/… We compile arguments and evidence from benchmarking age- & sex-prediction on > 2600 EEGs from two large public datasets. We found that basic artifact rejection consistently led to better model performance, whereas removal of ocular and muscle artifacts hampered performance. As it turns out that those peripheral signals are predictive themselves! Our results therefore argue in favor of the need to diligently process EEG data, if the goal is to have brain-specific biomarkers (and if prediction is not the only objective). Our efforts to build more interpretable #ML models for EEG led us to extending the established Morlet wavelet methodology for spectral analysis of EEG to accommodate state-of-the-art ML models based on covariance matrices. This allowed us to perform head-to-head comparisons between classical EEG features and frequency-specific model predictions for, both, brain and artifact signals. Compared to classical band-pass filtering, wavelets even led to improvements in prediction performance. Joint work with Philipp Bomatter, @JP4illard, Pilar Garces & Jörg F Hipp.