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- W4317209905 abstract "Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks." @default.
- W4317209905 created "2023-01-18" @default.
- W4317209905 creator A5046539917 @default.
- W4317209905 date "2023-01-18" @default.
- W4317209905 modified "2023-10-14" @default.
- W4317209905 title "Hyperbolic matrix factorization improves prediction of drug-target associations" @default.
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- W4317209905 doi "https://doi.org/10.1038/s41598-023-27995-5" @default.
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