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. 2019 Sep 3;10:726. doi: 10.3389/fgene.2019.00726

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Copyright © 2019 Maj, Azevedo, Giansanti, Borisov, Dimitri, Spasov, Alzheimer’s Disease Neuroimaging Initiative, Lió and Merelli

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ADNI1-GWAS cross-tissues performance. By column we can observe how much a model trained on a tissue is able to recognize without mistakes (AUC) AD/non-AD subjects from data related to different tissues. On the diagonal for each tissue the AUC obtained for that model during the training is reported. The top panel reports the cross-tissue results without any filter application, the middle panel when using List-PrediXcan and the bottom using List-unsupervised.