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#bioinformatics

8 Beiträge8 Beteiligte0 Beiträge heute
Öffentlich
Luke Pembleton

Anyone else finding the sequencing costs appear to be getting more expensive rather then continuing to decline. I'm thinking its likely related to inflation, wages and current US uncertainty...? oh and Illumina and alike only delivering true reduced cost/GB on large scale flowcells, which aren't suitable for many small to medium size projects...
#genomics #bioinformatics

Öffentlich
Bose-Einstein-Kondensat

A few images from yesterdays first #HoloDeck presentation at Prof. Varshneys #lab at the #Indian Institute of #Science (#IISc) in #Bangalore (#India), where people enjoyed 'playing molecules'.

Holodeck, also known as the Rechenkraft.net #Molecular #AR Environment is our #OpenSource approach - based on the @tiltfive hardware - to visualize molecular #structures in #3D for #education & #research & #development purposes.

#DIYbio#instruments#bioinformatics
Öffentlich
CSBJ

🧬Can we trust AI in bioinformatics if we don’t understand how it makes decisions?

As AI becomes central to bioinformatics, the opacity of its decision-making remains a major concern.

🔗 Demystifying the Black Box: A Survey on Explainable Artificial Intelligence (XAI) in Bioinformatics. Computational and Structural Biotechnology Journal, DOI: doi.org/10.1016/j.csbj.2024.12

📚 CSBJ: csbj.org/

Demystifying the Black Box: A Survey on Explainable Artificial Intelligence (XAI) in Bioinformatics. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2024.12.027
#XAI#Bioinformatics#AIethics
Öffentlich
Luke Pembleton

Cloud computing: where you're not just a bioinformatician, you’re also the finance manager, compliance officer, and IT support, all while carrying a hefty dose of mental fatigue. I miss those PhD days with a inhouse HPC when all you had to do was sbatch every parameter combo under the sun to test your research question… then complain about queue times and who was clogging the damn cluster with their BLAST jobs 😅
#bioinformatics

Öffentlich
Bioconductor

🎉 Announcing the EuroBioC2025 Sticker Design Winner! 🎉

🏆 Winner: Chaima Hkimi

🎨 Chaima’s design draws on some of Barcelona’s most iconic landmarks, featuring the Sagrada Família and the vibrant colours of Park Güell. She even swapped the 'i' in 'BioC' for the Bioconductor logo — a creative nod to the project!

Thanks to everyone who submitted a design. You’ll be able to pick up Chaima’s sticker at EuroBioC2025!

EuroBioC2025 hex sticker design featuring the Sagrada Família in the centre, vibrant mosaic patterns inspired by Park Güell at the bottom, and other Barcelona landmarks like the crane and towers. The Bioconductor logo replaces the "i" in "BioC", and "bioconductor.org" is written across the top left. The overall shape is a yellow-bordered hexagon.
#EuroBioC2025#Bioconductor#Bioinformatics
Öffentlich
Christian Meesters

Busy year:

- Workflow programming for Data Analysis on #HPC Systems (Course in Mainz in January): ✅
- Same Course in Dresden (February) ✅
- #SnakemakeHackathon2025 at the CERN in March: ✅
- upcoming: #OpenScience Retreat (no hashtag, yet?) in April
- International Supercomputing Conference in June (so, @boegel, I will be there, after all and hope to meet people from @irods, too ; will you be there folks from #iRODS ?)
- German Conference for #Bioinformatics and NHR Conference in September

And I do not know whether this will be all. I have a nagging feeling there is more to come 😉

Öffentlich
Eli Roberson (he/him)

#Genomics
#Bioinformatics
#Academia

Beware US academic coders. If you have collaborators contributing code from a sanctioned region, your repos may be locked.

See mastodon.social/@organicmaps/1

The kicker is that the US is leaning authoritarian and all in on censorship. If Canada or Mexico become sanction targets and you have code pushes from there, Microsoft will lock you out of your GitHub accounts.

GitHub read-only
MastodonOrganic Maps (@organicmaps@mastodon.social)Attached: 1 image We have a temporary glitch with GitHub—probably some contributor was geolocated in a sanctioned region (no details yet). All required documents to unlock the account have been uploaded. Don't blame Microsoft/GitHub - it is just U.S. law. Please be patient. It should be unblocked soon.
Öffentlich
Bioinfhotep

link.springer.com/article/10.1
#networksAnalysis #rstats #methods #bioinformatics

SpringerLinkCross-validation for training and testing co-occurrence network inference algorithms - BMC BioinformaticsBackground Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation. Results Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability. Conclusions Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method’s applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health.
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