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1/12 🧵 Quick thread: a heads-up for anyone working with high-dimensional omics data and multiple testing — FDR correction can sometimes lie to you. A thread based on our recent publication in @GenomeBiology : doi.org/10.1186/s13059…
Chakravarthi Kanduri
@KanduriC
Researcher @unioslo_bioinfo | PhD in #Bioinformatics from @helsinkiuni | academic #datascience, #python, #rstats, #ML
422 Following 336 Followers 309 Posts
Latest posts 5
Out now in @GigaScience after substantial improvement since the first version: doi.org/10.1093/gigasc…. Improvements since the first version are briefly summarised in the 🧵
academic.oup.com Profiling the baseline performance and limits of machine learning models for adaptive immune... AbstractBackground. Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) h AIRR ML: Did you know that baseline machine learning methods can perform well even when disease-associated signals are present in only 2 out of 100 000 receptors in diseased adaptive immune repertoires? Check our latest preprint: biorxiv.org/content/10.110… 1/10View quoted post on X
Excited!
AIRR ML: Did you know that baseline machine learning methods can perform well even when disease-associated signals are present in only 2 out of 100 000 receptors in diseased adaptive immune repertoires? Check our latest preprint: biorxiv.org/content/10.110… 1/10
Essential read for everyone programming in science where there is often a trade-off between speed vs quality. Great work by @GabeBalaban @IvarGrytten @kuppern87 @LonnekeScheffer and @SandveGeir.
Often feel guilty writing quick and dirty code? Don’t be! Or not necessarily..! Learn when and where to go for quality and when to go for speed - "Ten simple rules for quick and dirty scientific programming": journals.plos.org/ploscompbiol/a…View quoted post on X
