As a proof of concept, we successfully annotate three previously unidentified compounds frequently found in human samples

Our results demonstrate that SingleFrag surpasses state-of-the-art in silico fragmentation tools, providing a powerful method for annotating unknown MS/MS spectra of known compounds

Leveraging the model’s efficiency, we created a database of 8.2M synthetically generated molecules and conducted a Turing-like test with organic chemistry experts to further assess the plausibility of the generated molecules, and potential biases and limitations of #CoCoGraph

#CoCoGraph outperforms state-of-the-art approaches on standard benchmarks while requiring up to an order of magnitude fewer parameters

At #NetSci2025 @netsciconf.bsky.social today? Don't miss Gemma Bel's poster at the Network Neuroscience satellite

📰Model-based alignment of developing connectomes
📍FaSos GG76S 1.018
🕔5:30pm

At #NetSci2025? Don't miss Teresa Lazaros's talk today at the Network Neuroscience satellite

📰 Probabilistic network alignment applied to brain connectomes
📍 FaSos GG76S 1.018
🕔 5pm

🔗 to paper: dx.doi.org/10.1038/s414...

At #NetSci2025 today? Don't miss Manuel Ruiz-Botella's talk at the @netbiomed2025.bsky.social‬ satellite

📰 CoCoGraph: A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules
📍 FaSos FaSoS GG76 1.02
🕒 3:30pm

🔗 to paper: doi.org/10.48550/arX...

As a consequence of these, our approach leads to better alignment than the state of the art on synthetic and real networks

With respect to existing methods for "graph matching":

1️⃣ Our approach naturally allows for the alignment of many networks

2️⃣ All assumptions are explicit and can be adapted to specific scenarios

3️⃣ It results in a posterior over alignments, rather than a single alignment

Can simple closed-form mathematical models predict human mobility as well as deep learning? In a new paper in
@naturecomms.bsky.social we show that the answer is YES

Human mobility is well described by closed-form gravity-like models learned automatically from data www.nature.com/articles/s41...