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  1. forestglip

    Comparing DNA Methylation Landscapes in Peripheral Blood from [ME/CFS] and Long COVID Patients, 2025, Peppercorn et al

    Did they group the ME/CFS and LC cohorts together before doing the significance test to pick 3663 DMFs that were differentially methylated compared to HC? If so, I don't immediately see an issue. Separation from the HCs on the plot would be expected, but not between LC and ME/CFS, I think. If...
  2. forestglip

    Using Heart rate monitoring to help with pacing.

    Merged thread Heart Rate Analysis for ME/CFS St. Denis, Catherine Abstract The article explores Catherine St. Denis's Heart Rate Analysis for ME/CFS, a hybrid work blending medical insight with poetic reflection to illuminate life with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Topics...
  3. forestglip

    PolyBio Spring 2025 Symposium

    Now on Youtube: 00:00 Amy Proal–An overview of PolyBio’s complex chronic illness research & clinical trials program 10:35 Resia Pretorius–Heterogenous fibrinaloid complexes (microclots): characterizing different phenotypes 19:46 Mark Painter–T cells as biosensors of viral persistence in Long...
  4. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    Oh, I don't think this will tell us much. Most of the genes in those gene sets were not important, and the gene sets themselves might not be the best groupings of the genes that were important. Potentially, the overall gene sets/pathways themselves might provide clues, but I wouldn't do anything...
  5. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    Just want to be sure I understand. You noted down all genes that are included in any of the top gene sets? And identified if they're duplicated in what way?
  6. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    I have the same concern about the test set being so small. I wouldn't say their model definitely wouldn't replicate on UK BioBank cohort based on that. The comparison they and I did on the BB is much less sophisticated than if they had actually used their model for classification. Edit: But...
  7. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    I thought it might be interesting, but when I go to Genebass and filter by any genes with "NOTCH" in the name, none of the six NOTCH genes are significant at all.
  8. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    Final thing for now because I am exhausted. I ran GSEA with the cellular component collection since I already ran the same one on the Genebass data. Link to list of enriched component gene sets in Genebass I decided not to use collapsePathways here. Since I am comparing if any gene sets match...
  9. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    Ok, I've run GSEA on the Zhang genes ranked by attention scores with the hallmark and canonical pathways collections: Hallmark: Canonical Pathways: I used collapsePathways to reduce the number of pathways, and it removes about half of them. Interestingly the first two, which seem very...
  10. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    I'm not sure it's the strongest evidence, but if, as @Utsikt says, there is overlap in diagnosis, and it's not considered a totally unrelated condition, I think that supports the idea that it got number 1 most related because it actually does have genes in common with the ME/CFS group.
  11. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    I think we can only go by the scores in the final model, and it'd be tough to get any specific individual protein interactions from that.
  12. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    I have a basic familiarity. Currently working on getting acquainted with fgsea in R so that I can use the collapsePathways function, which seems useful, and the software I was using doesn't have it. I'd appreciate the help, but no rush!
  13. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    Oh absolutely spend time on the important things. Maybe eventually I'll try to figure out how exactly they did it.
  14. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    I got the impression they made 1261 modules using the entire STRING network, then tested each one against the 115 genes and got 4 significant modules.
  15. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    Ok yes thank you that makes sense. Yes, that'd be ideal, and actually seems like what they did for their module enrichment of the top 115 genes. I don't understand a lot of the terms like Louvain, but it seems like they made discrete modules from STRING, then used Enrichr on the best matches. I...
  16. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    Sure, but isn't that what's interesting? The networks of related genes, even if they include genes not actually very useful on their own in this cohort. Which networks did the model "pull higher up" based on its assessment that these networks are useful for classification. Maybe the divide is...
  17. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    I get what you're saying, and will have to learn/think about how violating independence may affect the results. Intuitively, it feels like it should work to figure out how any prespecified groups of items are found more near the beginning of any long list of items, which I think is exactly...
  18. forestglip

    Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

    @jnmaciuch Do you think GSEA on the Zhang genes might be useful and/or which specific gene set collections would be most useful? They did an enrichment analysis of the top 115 genes and got associations with synapses, proteasomes, and these two: I assume the 115 gene cutoff is somewhat...
  19. forestglip

    Patients with severe ME/CFS need hope and expert multidisciplinary care, 2025, Miller et al

    For what it's worth, the author of the "AI-like" RR responded:
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