The occurrence of PI is affected by local factors such as implant surface characteristics hence the careful assessment of the local factors should be performed within treatment planning.

It can be concluded that plaque induced and prosthetically and surgically triggered peri-implantitis are different entities associated with distinguishing predictive profiles; hence, the appropriate causal treatment approach remains necessary. The advanced data mining model developed in this study seems to be a promising tool for diagnostics of peri-implantitis subtypes.

No abstract

This study showed that microbiological profile at all three experimental sites is differently characterized between patients suffering peri-implantitis and healthy controls. Data mining analysis identified Parvimonas micra as a highly accurate predictor of peri-implantitis when present in peri-implant pocket while this method generally seems to be promising for diagnosis of such complex infections.

Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency.