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. 2022 Aug 24;38(20):4771–4781. doi: 10.1093/bioinformatics/btac578

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© The Author(s) 2022. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fig. 6. — Disease enrichment in novel gene predictions: curves showing the number (no.) of known disease genes (as per DisGeNET; y-axis) recovered in top-k predicted genes (as per SVM score ranking; x-axis) of the corresponding hormone; focusing on all (lighter-shade red) versus novel (darker-shade black) predicted genes of the hormone. Our model (solid curves) performs better than chance recovery of disease genes by a random classifier (dashed lines). Only genes predicted for a hormone with SVM score>0 are considered here; all protein-coding genes are considered in Supplementary Figure S2 (A color version of this figure appears in the online version of this article.)