UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction.

BACKGROUND: One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate -in a function-specific fashion- the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships. RESULTS: We address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks -e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks. CONCLUSIONS: UNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.

The role of cryptocurrency in the dynamics of blockchain-based social networks: The case of Steemit.

A shift of paradigm is running over online social platforms: the over-centralization of these platforms is leaving room for decentralized solutions based on blockchain technologies, such as blockchain-based online social networks-BOSNs. Among the many unknown aspects of these techno-social systems, the objective of this study is to propose an analytical framework to assess the impact of the cryptocurrencies linked to a BOSN platform on the evolution of its social network and on the behavior of their users, in terms of production of content and/or its promotion through a voting and rewarding system. The framework has been applied to Steemit, one of the most widespread BOSNs, from which we collected three-year-long high-resolution data on its evolution along with the price of its main cryptocurrencies. On users' activities extracted from these longitudinal data, we applied a time-series correlation analysis and a correlation analysis between the action allocation strategies and the obtained rewards, in the case of most central accounts. The analysis has highlighted pieces of evidence of the influence of the cryptocurrency price on users' actions, particularly on actions that shape the structure of the social networks. Second, we also found highly rewarded users prefer actions related to the promotion of content rather than the creation of high-quality content, exploiting the reward distribution mechanisms implemented by the platform. These findings highlight that the shift of paradigm towards blockchain and cryptocurrency technologies might strengthen the influence of financial and economic factors rather than relational/social aspects on the evolution of these new complex techno-social systems.

Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools.

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.