Association of DNA methylation and musculoskeletal spinal pain at four timepoints during adolescence and early adulthood This study examined the association between epigenetic genome-wide DNA methylation with mechanism-based phenotypes of spinal pain from adolescence through early adulthood, accounting for confounder...

Our paper looking at the Association of DNA methylation and musculoskeletal spinal pain at four timepoints during adolescence and early adulthood #ewas www.tandfonline.com/eprint/7WDSY...

Be kind to each other.. bad news for most coming in under embargo. We need more funding in the system...

📣 The call for tutorials & workshops at #ECCB2026 is now open! Share your tools, methods or expertise with the community.
🗓️ Deadline: 5 January 2026
👉 Submit: tinyurl.com/tw-eccb26

💡 ECCB will take place on 31 Aug–4 Sept in Geneva, gathering 1,000+ scientists from academia, industry, & healthcare.

ABACBS 2025 Conference Adelaide, South Australia. Nov. 24-

Last day to register for #ABACBS2025. Dont miss out on all of the fun! www.abacbs.org/abacbs2025

CSIRO in Hobart is hiring an #eDNA data specialist! Great team with wonderfully nice people (I can vouch for that), and what a location
www.linkedin.com/jobs/view/43...

ABACBS 2025 Conference Adelaide, South Australia. Nov. 24-

🚨 Abstract deadline extended! 🚨

You’ve now got until August 15 to submit for #ABACBS2025 — one-time extension only!
Join us in Adelaide for world-class #bioinformatics, workshops, student & EMCR events, and more.

🔗 www.abacbs.org/abacbs2025

Longitudinal Prediction of BMI using Explainable AI: Integrating Polygenic Scores, Maternal, Early-Life and Familial Factors Background/Objectives This study aimed to predict body mass index (BMI) trajectories from childhood to early adulthood using explainable artificial intelligence, integrating polygenic scores (PGS), ma...

Our preprint where we look for longitudinal prediction of #BMI using explainable #AI, specifically Kolmogorov-Arnold networks (#KANS) www.medrxiv.org/content/10.1...

Investigating the Duality of Interpretability and Explainability in Machine Learning The rapid evolution of machine learning (ML) has led to the widespread adoption of complex “black box” models, such as deep neural networks and ensemble methods. These models exhibit exceptional predi...

Interesting article describing the difference between interpretability and explainability in machine learning and what this means for #AI research ieeexplore.ieee.org/document/108...

Our explainable #AI preprint that uses a Kolmogorov-Arnold model (#KAN) to predict childhood #obesity using early-life factors and #polygenic risk.

From scientific theory to duality of predictive artificial intelligence models The author presents a conceptual framework for explainable artificial intelligence (XAI) encompassing model explanation, interpretation, and causality, guided by principles from scientific theory. He ...

From scientific theory to duality of predictive artificial intelligence models: Cell Reports Physical Science www.cell.com/cell-reports...