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Reposted by SEES Lab

Amaral Lab @amarallab.bsky.social · Aug 4

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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.

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Reposted by SEES Lab

SEES Lab @seeslab.bsky.social · Jul 14

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

academic.oup.com

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SEES Lab @seeslab.bsky.social · Jul 14

Yet another great collaboration with @oyanes.bsky.social

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SEES Lab @seeslab.bsky.social · Jul 14

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

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SEES Lab @seeslab.bsky.social · Jul 14

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

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SEES Lab @seeslab.bsky.social · Jul 14

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

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SEES Lab @seeslab.bsky.social · Jul 14

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

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SEES Lab @seeslab.bsky.social · Jul 14

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

academic.oup.com

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Reposted by SEES Lab

BioMassSpec @realbiomassspec.bsky.social · Jul 11

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation #BriedBioinform 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

academic.oup.com

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Reposted by SEES Lab

Manlio De Domenico @manlius.bsky.social · Jul 5

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.

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Reposted by SEES Lab

Manlio De Domenico @manlius.bsky.social · Jul 5

@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?...

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Reposted by SEES Lab

arXiv cs.LG Machine Learning @cslg-bot.bsky.social · May 23

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

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SEES Lab @seeslab.bsky.social · Jun 25

Great work by first author Manuel Ruiz-Botella!

SEES Lab @seeslab.bsky.social · Jun 25

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

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SEES Lab @seeslab.bsky.social · Jun 25

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

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SEES Lab @seeslab.bsky.social · Jun 25

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

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...

www.arxiv.org

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SEES Lab @seeslab.bsky.social · Jun 18

💡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...

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, ...

www.nature.com

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SEES Lab @seeslab.bsky.social · Jun 2

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

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SEES Lab @seeslab.bsky.social · Jun 2

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...

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SEES Lab @seeslab.bsky.social · Jun 2

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...

Reposted by SEES Lab

Yann Moalic @drym.bsky.social · May 16

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

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

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SEES Lab @seeslab.bsky.social · May 12

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

‘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

www.science.org

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Reposted by SEES Lab

Artime @oriartime.bsky.social · May 8

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...

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...

journals.aps.org

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Reposted by SEES Lab

Leto Peel @piratepeel.bsky.social · May 6

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...

Assistant/Associate Professor in Statistical Learning

vacancies.maastrichtuniversity.nl

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Reposted by SEES Lab

Jorge Bravo Abad @bravo-abad.bsky.social · May 3

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...

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.

open.substack.com

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