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. 2024 Nov 26;26(1):bbae611. doi: 10.1093/bib/bbae611

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© The Author(s) 2024. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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(A) AUROCs of LIMO-GCN in different parameters (the results are generated by using all negative samples). Comparison of different parameters influencing on LIMO-GCN performance. We systematically varied these parameters to understand their impact and identify the optimal settings for our disease gene prediction task. indicates the weight of GCN modules in the model. is to balance the loss function by combining L1 loss and cross-entropy loss. (B) AUPRCs of LIMO-GCN in different parameters. (C) No. of AD risk genes () in training data among top 100 ranked genes under different parameters.

Inline graphic — (A) AUROCs of LIMO-GCN in different parameters (the results are generated by using all negative samples). Comparison of different parameters influencing on LIMO-GCN performance. We systematically varied these parameters to understand their impact and identify the optimal settings for our disease gene prediction task. indicates the weight of GCN modules in the model. is to balance the loss function by combining L1 loss and cross-entropy loss. (B) AUPRCs of LIMO-GCN in different parameters. (C) No. of AD risk genes () in training data among top 100 ranked genes under different parameters.