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- W3125807201 abstract "Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help design protein-protein interaction inhibitors for therapy. Unfortunately, current machine learning methods to predict hot spots, suffer from limitations caused by gross errors in the data matrices. Here, we present a novel data pre-processing pipeline that overcomes this problem by recovering a low rank matrix with reduced noise using Robust Principal Component Analysis. Application to existing databases shows the predictive power of the method." @default.
- W3125807201 created "2021-02-01" @default.
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- W3125807201 date "2021-02-02" @default.
- W3125807201 modified "2023-10-13" @default.
- W3125807201 title "Robust principal component analysis‐based prediction of <scp>protein‐protein</scp> interaction hot spots" @default.
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- W3125807201 doi "https://doi.org/10.1002/prot.26047" @default.
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