Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome
This is the accompanying code for Cabassi et al. (2020). It is based on the code Zhao and Zucknick (2020) available at https://github.com/zhizuio/IPFStructPenalty/ . Part of this code was used for the multi-omic analysis of Seyres et al. (2020).
Cabassi, A. Seyres, D., Frontini, M., and Kirk, P.D.W. (2020). Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome. arXiv preprint. arXiv:2008.00235.
Seyres, D., et al. (2020). Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes. bioRxiv preprint. bioRxiv:2020.03.06.961805.
Zhao, Z. and Zucknick, M. (2020). Structured penalized regression for drug sensitivity prediction. Journal of the Royal Statistical Society: Series C (Applied Statistics).
Please feel free to contact me (Alessandra) should you need any help using this code. You can find my up-to-date email address in my GitHub profile.