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001     890265
005     20240625095126.0
024 7 _ |a 10.1002/prot.26047
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100 1 _ |a Sitani, Divya
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245 _ _ |a Robust Principal Component Analysis‐based Prediction of Protein‐Protein Interaction Hot spots ( RBHS )
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520 _ _ |a 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.
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700 1 _ |a Giorgetti, Alejandro
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700 1 _ |a Alfonso‐Prieto, Mercedes
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700 1 _ |a Carloni, Paolo
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773 _ _ |a 10.1002/prot.26047
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