A Web Graphical Tool for the Integration of Unbalanced Biomolecular Networks

1 MOTIVATION - The automated protein function prediction problem (AFP) is mainly characterized by the unbalance between annotated and unannotated genes and the integration of multiple data sources. The “informativeness” of each network/source may depend on the considered protein function, and neglecting the unbalance between annotated and unannotated proteins may thereby lead to strong decay in performance. Recently, the UNIPred algorithm [1] was proposed to integrate in a function-specific fashion the input networks by automatically handling the data imbalance. A relevant challenge in this context is the appropriate visualization and interpretation of the resulting network. Indeed, the network size can be extremely big and their simple visualization, with off-theshelf graphical visualization tools (e.g. GraphViz, GeneMania) produces a cloud of points hard to interpret and handle within a browser (lack of memory). To face the problem, here we propose a web-tool implementing UNIPred and introducing an approximate visualization of the graph. Having the system embedded different levels of abstraction, the user can both decide the part of the graph to explore, and click on the corresponding part to obtain a new enhanced visualization. 2. METHODS - The different input networks have been represented and stored according to the relational model. Efficient PL/SQL procedures calculate subgraphs centered on a vertex and with a given radius. An R software gathers the networks from the database, integrate them according to the UNIPred algorithm and store the results back in the database. Relying on this infrastructure, a web graphical tool has been implemented that offers different facilities to the user for the network management, their integration, visualization and exploration. In particular, both ``vertex-centric" exact and approximate visualizations are provided. With ``vertex-centric" [2] visualization approach we mean that the user can specify a vertex, named target, he wishes to explore the result of the integration, and the size of the subnetwork to extract. The extracted network can be shown to the user when its size is small and the available canvas is big enough for its visualization. Otherwise, approximate visualization techniques are used. For example, the target node can be connected with bubbles of different sizes that distribute the nodes of the subgraph relying on the weight of the outgoing edges and the distance from the target. Clicking on one of the bubbles, the visualization can be then expanded by showing other bubbles or single nodes. 3 RESULTS - We have realized a Web tool offering different facilities for the exploration of protein networks, their efficient integration using the UNIPred algorithm in a Web-based environment, and the exploration of the resulting network by means of a vertex-centric visualization approach. The visualization can be exact or approximate depending on the size of the network and of the drawing canvas. In the left part of Figure 1 a target is shown in the center and then four bubbles that partition the vertex at distance 1 from the target depending on the weight of the outgoing edges. This representation points out how much the co-functionality confidence degree has been propagated (or not) from the target node. Moreover, by clicking on one of the bubbles, it is split again in 4 parts, thus allowing multiple “vertex-centric” views at different resolution levels

GEO-RBAC : a spatially aware RBAC

Securing access to data in location-based services and mobile applications requires the definition of spatially aware access control systems. Even if some approaches have already been proposed either in the context of geographic database systems or contextaware applications, a comprehensive framework, general and flexible enough to cope with spatial aspects in real mobile applications, is still missing. In this paper, we make one step towards this direction and we present GEO-RBAC, an extension of the RBAC model to deal with spatial and location-based information. In GEORBAC, spatial entities are used to model objects, user positions, and geographically bounded roles. Roles are activated based on the position of the user. Besides a physical position, obtained from a given mobile terminal or a cellular phone, users are also assigned a logical and device independent position, representing the feature (the road, the town, the region) in which they are located. To make the model more flexible and re-usable, we also introduce the concept of role schema, specifying the name of the role as well as the type of the role spatial boundary and the granularity of the logical position. We then extend GEO-RBAC to cope with hierarchies, modeling permission, user, and activation inheritance

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification

A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks

Background: Several problems in network biology and medicine can be cast into a framework where entities are represented through partially labeled networks, and the aim is inferring the labels (usually binary) of the unlabeled part. Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalanced, that is one class is largely under-represented: for instance in the automated protein function prediction (AFP) for most Gene Ontology terms only few proteins are annotated, or in the disease-gene prioritization problem only few genes are actually known to be involved in the etiology of a given disease. Imbalance-aware approaches to accurately predict node labels in biological networks are thereby required. Furthermore, such methods must be scalable, since input data can be large-sized as, for instance, in the context of multi-species protein networks. Results: We propose a novel semi-supervised parallel enhancement of COSNet, an imbalance-aware algorithm build on Hopfield neural model recently suggested to solve the AFP problem. By adopting an efficient representation of the graph and assuming a sparse network topology, we empirically show that it can be efficiently applied to networks with millions of nodes. The key strategy to speed up the computations is to partition nodes into independent sets so as to process each set in parallel by exploiting the power of GPU accelerators. This parallel technique ensures the convergence to asymptotically stable attractors, while preserving the asynchronous dynamics of the original model. Detailed experiments on real data and artificial big instances of the problem highlight scalability and efficiency of the proposed method. Conclusions: By parallelizing COSNet we achieved on average a speed-up of 180x in solving the AFP problem in the S. cerevisiae, Mus musculus and Homo sapiens organisms, while lowering memory requirements. In addition, to show the potential applicability of the method to huge biomolecular networks, we predicted node labels in artificially generated sparse networks involving hundreds of thousands to millions of nodes

Making the analysis of the Italian legislative system easy : the ILMA web portal

The Italian Law-Making Archive, denoted ILMA, is a new web application for supporting the analysis of the Italian legislative processes. It aims to overcome the shortcomings that commonly affect quantitative analyses of legislative systems providing ready-to-use data on the Italian context organized in a relational structure and included in a unique repository. After having compared ILMA with other web information systems, the article describes the database architecture, proposes several examples of potential customized analyses that scholars may conduct through ILMA and, finally, explains its main functionalities for querying the database and exporting data

On the composition of digital licenses in collaborative environments

In the era of Web 2.0, users are not any longer just consumers of resources but they can actively produce, share and modify content, by composing and enhancing digital resources and services. In this context, the intellectual property of the users collaborating in authoring activities should be preserved. Starting from a model for digital licences generation and management useful in collaborative environments like the Web 2.0, in this paper we propose the algorithms of a DRM component responsible for the composition and modification of digital resources and the generation of the related licenses. Then, the paper presents a compliant architecture based on a composition of web services

Introducing cooperation and actions in amalgamated knowledge bases

The theory of amalgamated knowledge bases represents a formal logical foundation for heterogeneous databases. In an amalgamated knowledge base, data sources are modeled by generalized annotated logic. Moreover, an amalgamated knowledge base is equipped with a supervisor acting as a mediator for amalgamating knowledge from the local databases. Even if the framework is quite appealing, it does not model dynamic aspects. Moreover, no communication channels among local databases are supported and cooperation is provided only through the supervisor. In this paper, we extend the theory of amalgamated knowledge bases to deal with actions and cooperation among local database