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000890265 1001_ $$0P:(DE-Juel1)173773$$aSitani, Divya$$b0$$eCorresponding author$$ufzj
000890265 245__ $$aRobust Principal Component Analysis‐based Prediction of Protein‐Protein Interaction Hot spots ( RBHS )
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000890265 520__ $$aProteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help design protein-protein interaction inhibitors for therapy. Unfortunately, current machine learning methods to predict hot spots, suffer from limitations caused by gross errors in the data matrices. Here, we present a novel data pre-processing pipeline that overcomes this problem by recovering a low rank matrix with reduced noise using Robust Principal Component Analysis. Application to existing databases shows the predictive power of the method.
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000890265 7001_ $$0P:(DE-Juel1)165199$$aGiorgetti, Alejandro$$b1$$ufzj
000890265 7001_ $$0P:(DE-Juel1)169976$$aAlfonso‐Prieto, Mercedes$$b2
000890265 7001_ $$0P:(DE-Juel1)145614$$aCarloni, Paolo$$b3$$eCorresponding author
000890265 773__ $$0PERI:(DE-600)1475032-6$$a10.1002/prot.26047$$gp. prot.26047$$n6$$p639-647$$tProteins$$v89$$x1097-0134$$y2021
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