Alfred Ssekagiri

Metagenomic virus discovery is vital for early detection and global health preparedness. Applications and #scholarships for the 2026 MVD Workshop are now open. Join leading experts for hands‑on training across sequencing, analysis pipelines, and real‑world pathogen surveillance. 📍 Stellenbosch, South Africa from 12-16 Oc 2026 📅 Deadline: 30 April 2026 Apply here: ceri.org.za/news/?token=962 #CERITrainings

Work in diagnostics or are interested in scalable workflow solutions for clinical applications? Join our upcoming webinar💻 We'll explore operational challenges diagnostics teams encounter as testing demands scale, and how @nextflowio and Seqera help address them. 🔗 hubs.la/Q047h4Q_0

In London tomorrow? Join us for a day of FREE @nextflowio training and networking with #bioinformatics leaders across pharma and biotech. Plus, talks from: 🔹 @sanogenetics - Connecting Patients to Clinical Trials 🔹 @GenomicsEngland - How Seqera Enabled Genomics England’s New Aggregate 🔹 @SeqeraLabs - The Intelligent Engine for the Life Sciences seqera.io/events/seqera-…

A “computer” used to be a job title. Then a computer became a thing humans used. Now a computer is becoming a thing computers use.

UniProt Webinar Reminder Join us to learn how automatic annotation tools enrich UniProt protein data. Automatic annotation systems in UniProt 15.00 (GMT) Thursday 12th March. Registration is free but essential: ebi.ac.uk/training/event…

Introducing the Seqera AI CLI, your AI-powered terminal assistant for @nextflowio development.🚀Build locally, deploy to the cloud, debug intelligently. All from one interface.🧵hubs.la/Q045kG1M0

@nextflowio Podcast episode: Pipeline chaining, meta pipelines, and automation🎙️In this episode, we discuss strategies for connecting multiple Nextflow pipelines, from meta pipelines and unified DAGs to external orchestration approaches. The discussion covers technical considerations for meta pipelines in @nf_core, plus practical chaining approaches including @SeqeraLabs APIs, nf-cascade, and automation frameworks like #NodeRED Watch now: hubs.la/Q045cnL70

🎉 @AceUganda celebrates 2 PhD🎓 and 15 MSc🎓 #Bioinformatics graduates at this year's #Mak76thGrad 🥳! ​We are proud to support these academic journeys alongside @Makerere, @MakerereCHS and @IDIMakerere. 🧬 ​Congratulations to our newest Bioinformaticians✨🚀! #Graduation2026

The 𝐃anish 𝐃ata 𝐒cience 𝐀cademy D𝐞𝐚𝐝𝐥𝐢𝐧𝐞 𝐢𝐬 𝐌𝐚𝐫𝐜𝐡 4, 2026 ▪️ 10 PhD Fellowship Call : ddsa.dk/fellowships/ph… ▪️ 5 Postdoc Fellowship Call: ddsa.dk/postdocfellows…

I strongly encourage ECRs to apply for opportunities like the Travelling Fellowships from the ever supportive @Co_Biologists. They are fantastic for learning new skills, building collaborative networks, and honing scientific writing. doi.org/10.1242/dev.20…

New from the PHA4GE Wastewater Subgroup: a preprint on community-led standards for global wastewater-based infectious disease surveillance. 💧 Read here: zenodo.org/records/177823… Feedback welcome via GitHub: github.com/pha4ge/wastewa…. #PHA4GE #WastewaterSurveillance

scPRINT-2: Towards the next-generation of cell foundation models and benchmarks biorxiv.org/content/10.648…

An excellent and free tutorial on conducting systematic reviews by the University College London: Covers: • What a systematic review is • Types of systematic reviews • Stages in a systematic review • Apps for systematic reviews linkedin.com/feed/update/ur…

The Rising Stars Symposium (by @ibfg_es in Salamanca 🇪🇸) is an innovative meeting designed to identify & attract outstanding young researchers. We're happy that our lab member Liviana Ricci will take part of this event & present her work. Read more: readymag.website/u1842310954/58…

This is one of the best short articles I've read on P-values, CIs, and significance tests

🥁 NEW publication out nature.com/articles/s4158… based on the ZOE PREDICT cohorts involving  33,596 healthy people from 5 cross-sectional studies from 🇬🇧 &🇺🇸 with gut microbiome samples, detailed individual information and dietary habits! Check out the 🧵of key findings ⬇️

@CanaryMugume We have control of our data as health and no personal data will be shared .Aggregated data will be shared

This paper really is groundbreaking. It solves a long-standing embarrassment in machine learning: despite all the hype around deep learning, traditional tree-based methods (XGBoost, CatBoost, random forests, etc) have dominated tabular data—the most common data format in real-world applications—for two decades. Deep learning conquered images, text, and games, but spreadsheets remained stubbornly resistant. This paper's (published in Nature by the way) main contribution is a foundation model that finally beats tree-based methods convincingly on small-to-medium datasets, and does so very fast. TabPFN in 2.8 seconds outperforms CatBoost tuned for 4 hours—a 5,000× speedup. That's not incremental; it's a different regime entirely. The training approach is also fundamentally different. GPT trains on internet text; CLIP trains on image-caption pairs. TabPFN trains on entirely synthetic data—over 100 million artificial datasets generated from causal graphs. TabPFN generates training data by randomly constructing directed acyclic graphs where each edge applies a random transformation (using neural networks, decision trees, discretization, or noise), then pushes random noise through the root nodes and lets it propagate through the graph—the intermediate values at various nodes become features, one becomes the target, and post-processing adds realistic messiness like missing values and outliers. By training on millions of these synthetic datasets with very different structures, the model learns general prediction strategies without ever seeing real data. The inference mechanism is also unusual. Rather than finetuning or prompting, TabPFN performs both "training" and prediction in a single forward pass. You feed it your labeled training data and unlabeled test points together, and it outputs predictions immediately. There's no gradient descent at inference time—the model has learned how to learn from examples during pretraining. The architecture respects tabular structure with two-way attention (across features within a row, then across samples within a column), unlike standard transformers that treat everything as a flat sequence. So, the transformer has basically learned to do supervised learning. Talk to the paper on ChapterPal: chapterpal.com/s/a1899430/acc… Download the PDF: nature.com/articles/s4158…

The GA4GH Genomics Knowledge Standards (GKS) Work Stream aims to develop a “common language” to describe and share variants, helping to deliver on the promise of scalable genomic medicine. Watch the animation to learn more! buff.ly/9iA52Mf

Getting Rosalind Franklin’s story right is crucial, because she has become a role model for women going into science nature.com/articles/d4158…