We are on the cover of Nucleic Acids Research! doi.org/10.1093/nar/... Thanks to the Karakach team! (www.karakachlab.org) @dalhousieu.bsky.social. Cover image is a watercolour by my mom, Iudi Simon. A self-taught painter and a true talent. Thanks @narjournal.bsky.social for showcasing her work!
Find in @narjournal.bsky.social our latest kPPA-CART, a new effective framework for your multi-omics analysis. 👉https://academic.oup.com/nar/article/doi/10.1093/nar/gkaf844/8257763?utm_source=authortollfreelink&utm_campaign=nar&utm_medium=email&guestAccessKey=44031ac6-ca6c-47a7-bea5-f5a13e8e45e3
Augmented kurtosis-based projection pursuit: a novel, advanced machine learning approach for multi-omics data analysis and integration
Abstract. Due to the heterogeneity of multi-omics data, exacting their maximum information potential remains a challenge. Whereas some solutions have been
academic.oup.com
Dr. Tobias Karakach@karakachlab.bsky.social · 11mo agoLike Dj Khalid, another one from the KarakachLab! We just dropped kPPA-CART in @narjournal.bsky.social, a new machine learning method that finds hidden signals in messy multi-omics data. From kidney aging 🧬 to breast cancer 🎗️, it uncovers patterns others miss. doi.org/10.1093/nar/...
Proof? • Improved breast cancer subtyping vs PAM50 • Links to survival + tumor mutational burden • Free R package + web app for the community 👉 www.karakachlab.org/software/int...
The solution: kPPA-CART It digs into those low-signal regions, integrates transcriptomics, proteomics & methylation data, and finds real biological meaning others miss.
What’s the problem? 🤔 Most computational tools in bioinformatics only detect strong, obvious signals. Subtle, low-intensity features (the ones that really matter for biology) get lost in the noise.
Like Dj Khalid, another one from the KarakachLab! We just dropped kPPA-CART in @narjournal.bsky.social, a new machine learning method that finds hidden signals in messy multi-omics data. From kidney aging 🧬 to breast cancer 🎗️, it uncovers patterns others miss. doi.org/10.1093/nar/...