Multi-omics data have revolutionized biomedical research by providing a comprehensive understanding of biological systems and the molecular mechanisms of disease development. However, analyzing multi-omics data is challenging due to high dimensionality and limited sample sizes, necessitating proper data-reduction pipelines to ensure reliable analyses. Additionally, its multimodal nature requires effective data-integration pipelines. While several dimensionality reduction and data fusion algorithms have been proposed, crucial aspects are often overlooked. Specifically, the choice of projection space dimension is typically heuristic and uniformly applied across all omics, neglecting the unique high dimension small sample size challenges faced by individual omics. This paper introduces a novel multi-modal dimensionality reduction pipeline tailored to individual views. By leveraging intrinsic dimensionality estimators, we assess the curse-of-dimensionality impact on each view and propose a two-step reduction strategy for significantly affected views, combining feature selection with feature extraction. Compared to traditional uniform reduction pipelines in a crucial and supervised multi-omics analysis setting, our approach shows significant improvement. Additionally, we explore three effective unsupervised multi-omics data fusion methods rooted in the main data fusion strategies to gain insights into their performance under crucial, yet overlooked, settings.

Cutting-edge technologies in RNA biology are pushing the study of fundamental biological processes and human diseases and accelerate the development of new drugs tailored to the patient's biomolecular characteristics. Even if many structured and unstructured data sources report the interaction among different RNA molecules and some other biomedical entities (e.g., drugs, diseases, genes), we still lack a comprehensive and well-described RNA-centered Knowledge Graph (KG) that contains such information and sophisticated services that support the user in its creation, maintenance, and enhancement. This PhD project aims to create a biomedical KG (named RNA-KG) to represent, and eventually infer, biological, experimentally validated interactions between different RNA molecules. We also wish to enhance the KG content and develop sophisticated services designed ad-hoc to support the user in predicting uncovered relationships and identifying new RNA-based drugs. Services will rely on deep learning methods that consider the heterogeneity of the graph and the presence of an ontology that describes the possible relationships existing among the involved entities. Moreover, we will consider Large Language Models (LLMs) in combination with RNA-KG for interacting with the user with the ground truth information contained in our KG for extracting relationships from unstructured data sources.

Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms. The advent of high-throughput technologies, enabling us to acquire high-dimensional views of the same patients (e.g. omics data, laboratory data, imaging data), calls for the development of data fusion techniques for PSNs in order to leverage this rich heterogeneous information. In this article, we review existing methods for integrating multiple biomedical data views to construct PSNs, together with the different patient similarity measures that have been proposed. We also review methods that have appeared in the machine learning literature but have not yet been applied to PSNs, thus providing a resource to navigate the vast machine learning literature existing on this topic. In particular, we focus on methods that could be used to integrate very heterogeneous datasets, including multi-omics data as well as data derived from clinical information and medical imaging.

The attribute-based access control (ABAC) model has been gaining popularity in recent years because of its advantages in granularity, flexibility, and usability. Few approaches based on association rules mining have been proposed for the automatic generation of ABAC policies from access logs. Their aim is the identification of policies that do not overfit over training data, are not too general and thus does to disclose sensitive resources to everyone, and are interpretable by humans. The large ABAC privilege space along with the sparsity and unbalance distribution of the available logs make the solution of this task particularly complex and current approaches have different limitations. In this paper we compare different symbolic and nonsymbolic machine learning (ML) techniques for inferring ABAC policies and discuss their pros and cons. Based on experimental results on a toy dataset and on a real dataset, we argue that which is the best technique depends on the characteristics of the considered data. When the data are highly separable according to PCA and t-SNE decomposition, the quality of the obtained ABAC policies is higher and also policies are easily interpretable. By contrast, when this property does not hold, the quality of the obtained policies is low; in this case, non-symbolic ML techniques show better results than the symbolic ones.

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.

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.

ETL (Extraction-Transform-Load) tools, traditionally developed to operate offline on historical data for feeding Datawarehouses, need to be enhanced to deal with big and fresh data and be executed at network level during data streams acquisition.In this paper, we present StreamLoader, a Web application for the specification of conceptual ETL dataflows on heterogeneous sensor data that leverages the peculiarities of network configuration, data stream management, and specification and deployment of ETL operations in a programmable network.It can be used for feeding traditional/ real-time data-warehouses or visual analytic tools.

The visualization and analysis of big bio-molecular networks is a key feature for the investigation and prediction of protein functions in a multi-species context. In this paper we present the design of a system that integrates data management, machine learning and visualization facilities to make effective the visual analysis of big networks by means of web-based interfaces.

UNLABELLED: RANKS is a flexible software package that can be easily applied to any bioinformatics task formalizable as ranking of nodes with respect to a property given as a label, such as automated protein function prediction, gene disease prioritization and drug repositioning. To this end RANKS provides an efficient and easy-to-use implementation of kernelized score functions, a semi-supervised algorithmic scheme embedding both local and global learning strategies for the analysis of biomolecular networks. To facilitate comparative assessment, baseline network-based methods, e.g. label propagation and random walk algorithms, have also been implemented. AVAILABILITY AND IMPLEMENTATION: The package is available from CRAN: https://cran.r-project.org/ The package is written in R, except for the most computationally intensive functionalities which are implemented in C. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The quantity and quality of the information represented by means of graph structures is more and more increasing nowadays. Merging this information assumes a deep relevance in order to identify unknown relationships and properties and conduct different kinds of analysis. However, this task is complicated due to the variety of formats with which graphs are represented and their size. In this paper we propose MergeGraphs, a web based system in which the user can graphically drag and drop heterogeneous graphs and specify transformation and merging operations (union, intersection and join). The obtained execution plan is translated in Pig Latin scripts and executed in a cluster of machines. The use of Google App/Compute Engines allow to develop a scalable distributed approach.

With the ambition of providing teachers with a concrete tool for worldwide exploiting didactic contents to feature their courses, we face the problem of creating a social platform with adequate functionalities to satisfy the teacher expectations. Starting with a well designed architecture we endow it with three key functionalities that become the stakeholders of the emerging social network: 1) a quality system ensuring the value of the materials the users put in the platform repository as their contribution to the social business, 2) a recommender system based on computational intelligence techniques constituting the principal tool to guide teachers along the assembling of materials into courses, and 3) a gamification system, root of the no-profit business plan of the platform, to involve teachers in the social network. As a result we delineate an ecosystem where teachers exploit contents of a repository to which contribute by themselves. They are encouraged in exploiting and contributing because the contents are of high quality; they are wisely assisted in the exploration of the repository by platform services yet under their full control; and they are variously reworded by this involvement.

In this paper we introduce a platform tailored to give teachers and trainers appropriate knowledge, skills, and innovative tools in the domain of the entrepreneurial education. To this end, a social network is created where teachers can formally or informally share experiences supporting their peers with technical training, along with theory and practical examples deriving from mutual and practical experiences in entrepreneurship. In this perspective, the platform through a systematic and intelligent use of metadata is able to offer an innovative social network specially tailored for teachers in order to valorize their competencies and to fit their expectations.

SUMMARY: We present GOssTo, the Gene Ontology semantic similarity Tool, a user-friendly software system for calculating semantic similarities between gene products according to the Gene Ontology. GOssTo is bundled with six semantic similarity measures, including both term- and graph-based measures, and has extension capabilities to allow the user to add new similarities. Importantly, for any measure, GOssTo can also calculate the Random Walk Contribution that has been shown to greatly improve the accuracy of similarity measures. GOssTo is very fast, easy to use, and it allows the calculation of similarities on a genomic scale in a few minutes on a regular desktop machine. CONTACT: [email protected] AVAILABILITY: GOssTo is available both as a stand-alone application running on GNU/Linux, Windows and MacOS from www.paccanarolab.org/gossto and as a web application from www.paccanarolab.org/gosstoweb. The stand-alone application features a simple and concise command line interface for easy integration into high-throughput data processing pipelines.

Schemas are often used to constrain the content and structure of XML documents. They can be quite big and complex and, thus, difficult to be accessed manually. The ability to query a single schema, a collection of schemas or to retrieve schema components that meet certain structural constraints significantly eases schema management and is, thus, useful in many contexts. In this paper, we propose a query language, named XSPath, specifically tailored for XML schema that works on logical graph-based representations of schemas, on which it enables the navigation, and allows the selection of nodes. We also propose XPath/XQuery-based translations that can be exploited for the evaluation of XSPath queries. An extensive evaluation of the usability and efficiency of the proposed approach is finally presented within the EXup system [9].

Ranking genes in functional networks according to a specific biological function is a challenging task raising relevant performance and computational complexity problems. To cope with both these problems we developed a transductive gene ranking method based on kernelized score functions able to fully exploit the topology and the graph structure of biomolecular networks and to capture significant functional relationships between genes. We run the method on a network constructed by integrating multiple biomolecular data sources in the yeast model organism, achieving significantly better results than the compared state-of-the-art network-based algorithms for gene function prediction, and with relevant savings in computational time. The proposed approach is general and fast enough to be in perspective applied to other relevant node ranking problems in large and complex biological networks.

In many emerging XML application contexts and distributed execution environments (like disconnected and cloud computing, collaborative editing and document versioning) the server that determines the updates to be performed on a document, by evaluating an XQuery Update expression, is not always the same that actually makes such updates-represented as Pending Update Lists (PULs)effective. The process of generating the PUL is thus decoupled from that of making its effect persistent on the document. The PUL executor needs to manage several PULs, that, depending on the application context, are to be executed as sequential or parallel update requests, possibly relying on application-specific policies. This requires some capabilities of dynamic reasoning on updates. In the paper, we state the most relevant properties to reason on, develop the corresponding algorithms and present a PUL handling system, providing an experimental evaluation of this system.

Data on the Web mostly are in XML format and the need often arises to update their structure, commonly described by an XML Schema. When a schema is modified the effects of the modification on documents need to be faced. XSUpdate is a language that allows to easily identify parts of an XML Schema, apply a modification primitive on them and finally define an adaptation for associated documents, while Eχup is the corresponding engine for processing schema modification and document adaptation statements. Purpose of this demonstration is to provide an overview of the facilities of the XSUpdate language and of the Eχup system.

Applications that involve data integration among multiple sources often require a preliminary step of data reconciliation in order to ensure that tuples match correctly across the sources. In dynamic settings such as data mashups, however, traditional offline data reconciliation techniques that require prior availability of the data may not be applicable. The alternative, performing similarity joins at query time, is computationally expensive, while ignoring the mismatch problem altogether leads to an incomplete integration. In this paper we make the assumption that, in some dynamic integration scenarios, users may agree to trade the completeness of a join result in return for a faster computation. We explore the consequences of this assumption by proposing a novel, hybrid join algorithm that involves a combination of exact and approximate join operators, managed using adaptive query processing techniques. The algorithm is optimistic: it can switch between physical join operators multiple times throughout query processing, but it only resorts to approximate join operators when there is statistical evidence that result completeness is compromised. Our experiments show that sensible savings in join execution time can be achieved in practice, at the expense of a modest reduction in result completeness.

BACKGROUND: The today's public database infrastructure spans a very large collection of heterogeneous biological data, opening new opportunities for molecular biology, bio-medical and bioinformatics research, but raising also new problems for their integration and computational processing. RESULTS: In this paper we survey the most interesting and novel approaches for the representation, integration and management of different kinds of biological data by exploiting XML and the related recommendations and approaches. Moreover, we present new and interesting cutting edge approaches for the appropriate management of heterogeneous biological data represented through XML. CONCLUSION: XML has succeeded in the integration of heterogeneous biomolecular information, and has established itself as the syntactic glue for biological data sources. Nevertheless, a large variety of XML-based data formats have been proposed, thus resulting in a difficult effective integration of bioinformatics data schemes. The adoption of a few semantic-rich standard formats is urgent to achieve a seamless integration of the current biological resources.

A well-known problem of temporal databases is that the amount of stored data tends to increase very fast. Moreover, detailed data are useful when they are acquired but they often become less relevant after some time. In most cases, after a period of time only summarized data need to be kept, whereas detailed data expire and can be removed from the database. Multigranular temporal databases enhance the expressive power of temporal databases by supporting temporal attributes at different levels of detail. However, in existing approaches the level of detail of an attribute, that is its granularity, depends only on the attribute semantics and does not depend on how recent the attribute values are. This paper proposes an approach supporting the aggregation of different portions of the value of a temporal attribute at different levels of detail, and the deletion or the transfer to tertiary storage of old values at a given level of detail, in order to minimize disk storage occupancy. In the proposed multigranular temporal object-oriented data model, the expiration of attribute values at a given granularity can be specified, together with the action to take when data expire: either aggregation to a coarser granularity, or deletion of values, or both.

UCS-Router (E. Bertino et al.) is a policy engine developed for message dispatching in a Universal Communication System. A user of the system has a unique identifier, named Universal Communication Identifier (UCI), independent from the actual communication devices the user owns. It is a task of the UCS-Router to properly route messages or phone calls arriving at the UCI to the proper user devices. The routing process is performed according to policies the user specifies on the type of message, on the device capabilities and states, on the sender characteristics, and on the message sending time. We present a declarative policy language developed for UCS-Router. Users of UCS-Router can use this language for specifying message filtering and routing policies. Moreover, we present issues that can arise in the evaluation process of the policies. Such issues are primarily due to the high expressiveness of the language and to the possibility to handle a great variety of message media format. Finally, we present how the UCS-Router addresses such issues.

The main key feature of temporal databases is to maintain all values taken by object attributes over time. Since historical information may be needed at different levels of detail, multigranular temporal databases have been introduced, in which different attributes can be stored at different temporal granularities. An important issue that has not been addressed, however is that the required level of detail does not only depend on attribute semantics, rather it is often related to how recent the data are. It is quite natural that recent data are needed at greater level of detail, whereas less detail is needed as data age. As an extreme case, data can also expire, that is, they are no longer needed after a certain period of time. In this paper we address the problem of evolution and expiration of historical data in a multigranular temporal object model.

We report on a network traffic warehousing project at Telcordia. The warehouse supports a variety of applications that require access to Internet traffic data. The applications include Service Level Agreement (SLA), web traffic analysis, network capacity engineering and planning, and billing. We describe the design of the warehouse and the issues encountered in building the warehouse.