Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp

Result disambiguation in web people search

We study the problem of disambiguating the results of a web people search engine: given a query consisting of a person name plus the result pages for this query, find correct referents for all mentions by clustering the pages according to the different people sharing the name. While the problem has been studied extensively, we discover that the increasing availability of results retrieved from social media platforms causes state-of-the-art methods to break down. We analyze the problem and propose a dual strategy where we distinguish between results obtained from social media platforms and those obtained from other sources. In our dual strategy, the two types of documents are disambiguated separately, using different strategies, and their results are then merged. We study several instantiations for the different stages in our proposed strategy and manage to achieve state-of-the-art performance