SEES Lab

ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings Abstract. Machine learning offers a promising path to annotating the large number of unidentified MS/MS spectra in metabolomics, addressing the limited cov

ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings url: academic.oup.com/bib/article/...

🚀A ICREA tornem a estar actius a les xarxes socials! Parlarem de:

✨ Nous descobriments de la #ComunitatICREA
📢 Convocatòries
🎉 Esdeveniments i congressos
🧪 I molta recerca d'excel·lència!

Segueix-nos a X x.com/icreacommunity i LinkedIn www.linkedin.com/company/icrea i no et perdis cap novetat!

🧵
1/ New from @reeserichardson.bsky.social, @jabyrnesci.bsky.social, and our lab in @pnas.org :

A growing body of evidence shows that "systematic" scientific fraud is an emerging threat to the integrity of science.

Our latest study investigates how this fraud is organized and sustained.

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

New paper out in Briefings in Bioinformatics

📰SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation academic.oup.com/bib/article/...

Yet another great collaboration with @oyanes.bsky.social

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

Here, we present #SingleFrag, a novel deep learning tool that predicts individual MS/MS fragments separately, rather than attempting to predict the entire fragmentation spectrum at once

Identifying metabolites in MS/MS data often means matching experimental spectra with existing spectral libraries. But, with limited libraries, identifying unknowns remains a big hurdle in #metabolomics

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

New paper out in Briefings in Bioinformatics

📰SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation academic.oup.com/bib/article/...

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation Abstract. Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation #BriedBioinform academic.oup.com/bib/article/...

Thanks 🙏🙏 @martikagv.bsky.social @vcolizza.bsky.social @pessoabrain.bsky.social @asteixeira.bsky.social @gomezgardenes.bsky.social @seeslab.bsky.social (and all the speakers not in this platform) for their amazing contribution to make this edition a reference for young Network Scientists.

@mscxnetworks.bsky.social ended.

A week of cutting edge complexity science, from foundations to applications. Amazing speakers and great cohort of attendants.

One of the best editions ever. 10 years of passion, love and network science.

In an amazing piece of #Sicily

youtu.be/Nh5vrEKheH0?...

Manuel Ruiz-Botella, Marta Sales-Pardo, Roger Guimer\`a: A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules https://arxiv.org/abs/2505.16365 https://arxiv.org/pdf/2505.16365 https://arxiv.org/html/2505.16365

Great work by first author Manuel Ruiz-Botella!

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

A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules Developing new molecular compounds is crucial to address pressing challenges, from health to environmental sustainability. However, exploring the molecular space to discover new molecules is difficult...

New preprint out in the arXiv!

We introduce #CoCoGraph, a collaborative and constrained graph diffusion model capable of generating molecules that are guaranteed to be chemically valid www.arxiv.org/abs/2505.16365

Applied physics and mathematics The highlights include but are not limited to the research areas of electronics, optoelectronics, computing technologies and theories, soft matter physics, ...

💡Our paper on probabilistic alignment of networks has been highlighted by Nature Communications as one the 50 best papers recently published in the area of Applied physics and mathematics www.nature.com/collections/...

📰Read the paper www.nature.com/articles/s41...

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...

Probabilistic alignment of multiple networks - Nature Communications Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

#Networks www.nature.com/articles/s41...

‘Data manipulations’ alleged in study that paved the way for Microsoft’s quantum chip Internal emails from 2021 reveal tensions among researchers hunting for elusive Majorana particle

‘Data manipulations’ alleged in study that paved the way for Microsoft’s quantum chip | Science | AAAS www.science.org/content/arti...

Multiscale Field Theory for Network Flows A new theoretical framework reveals universal principles governing network flows, predicting a threshold where flow becomes unsustainable and uncovering how dissipation can enhance performance in cert...

So happy to share this one! Beautiful collaboration with @anduviera.bsky.social, @raissadsouza.bsky.social and Guram Mikaberidze on network flows: journals.aps.org/prx/abstract...

We're looking for a new colleague to join us in Maastricht as an Assistant/Associate Professor in statistical learning in our Data Analytics and Digitalisation department.

Deadline to apply: June 8th

vacancies.maastrichtuniversity.nl/job/Maastric...

How probabilistic modeling transforms our approach to aligning complex networks Networks are everywhere—from social connections among individuals, neural connections in brains, interactions among proteins in biological cells, to communication channels within large organizations.

New substack post: I discuss a recent innovative method—ProbAlign, introduced by Lázaro, Guimerà, & Marta Sales-Pardo—which uses probabilistic modeling to achieve more accurate, transparent, and flexible network alignments. open.substack.com/pub/bravoaba...

Probabilistic alignment of multiple networks
->Nature | More info from EcoSearch

Probabilistic alignment of multiple networks - Nature Communications Network alignment is a fundamental problem in several domains that aims at mapping nodes across networks. Here, the authors develop a probabilistic approach that assumes that observed networks are err...

www.nature.com/articles/s41...