Biological systems, composed of interconnected biological objects, can be effectively represented through a network graph-model where nodes and edges denote biological objects and their interactions/associations, respectively. In this context, Gene Co-expression Networks (GCNs) are widely studied, however, since they rely on a static representation of interactions, they fail to represent the evolution of time-dependent phenomena. The dynamic nature of gene expression is crucial for unravelling complex biological processes. To address this issue, we designed an open-source framework for constructing real-world Temporal Gene Co-expression Networks ( tGeCoNet ). Temporal Gene Co-expression Networks (TGCNs) are modelled from Genotype-Tissue Expression (GTEx) data provided by GTEx project. Gene expression data, along with metadata (e.g., age groups) is used to build a temporal network that captures the time-varying associations between genes (i.e., nodes), based on the statistical significance computed for each age group (i.e., time point). Designed as a scalable and reproducible solution, tGeCoNet can serve as a valuable data source for studies on aging, disease progression, and other time-dependent biological phenomena, especially for those exploring the temporal evolution of gene co-expression within the framework of network science.

In bioinformatics, data analysis often involves complex workflows that require processing vast amounts of biological data. Pipelines play a crucial role in automating and organizing these sequences of computational tasks, ensuring reproducibility, efficiency, and scalability. In this paper, we presented F LENP DESI gner ( fDESI ), an open-source and user-friendly web application for visual bioinformatics pipeline design. It allows intuitive pipeline creation through a drag-and-drop interface, generating pipeline descriptions in a JSON-based meta-language. Furthermore, it provides an all-in-one environment to overcome the need for external tools, beyond the third-party software within the pipeline itself. Our contribution includes a flexible framework for pipeline modelling, built-in functionalities requiring no programming expertise, an integrated execution engine, and an open-source graphical interface for streamlined bioinformatics workflow design.

Network alignment (NA) is a computational methodology employed to compare biological networks across different species or conditions. By identifying conserved structures, functions, and interactions, NA provides invaluable insights into shared biological processes, evolutionary relationships, and system-level behaviors. This manuscript presents a comprehensive overview of NA methodologies, including the importance of preprocessing network data, selecting suitable input formats, and understanding diverse network types such as attributed, temporal, and multilayer networks. Additionally, it explores key challenges such as seed nodes selection, algorithm configuration, and cross-species alignment, emphasizing the necessity of integrating functional annotations, sequence similarity, and network topology for biologically meaningful results. Various NA strategies, including Local and Global Network Alignment, are discussed alongside their respective advantages and limitations. Practical recommendations for effectively documenting and visualizing NA experiments are also provided, ensuring reproducibility and clarity in research. By leveraging diverse alignment tools and adopting best practices, researchers can unlock the potential of NA to advance our understanding of complex biological systems.

Adolescent idiopathic scoliosis (AIS) is a spinal deformity that tends to get worse as children grow and requires constant monitoring. Current AI models are trained on datasets consisting mainly of X-ray images of scoliosis patients, typically used to predict the Cobb angle. We aim to create a valuable and alternative dataset to traditional radiographic images that can be used to train AI models for both the detection and assessment of scoliosis, starting from images of scoliosis cases captured on smartphone and annotated. Our study may make up for limitations of invasive methods and address the challenge of lack of data for developing effective AI models.

Network graph models are a powerful tool for handling objects, in terms of interactions and relationships. For instance, in bioinformatics networks are applied for analysing complex biological systems, topologically, as well as for investigating their own homology. In this context, the pairwise network alignment can be performed for producing a set of node-to-node mappings from a source network to a target one. For instance, network alignment can be applied for porting knowledge from a simpler to a more complex system (e.g., a simpler biological organism to a complex one). In this paper, we presented a GPU-based method for the pairwise Local Network Alignment, implemented by using a greedy strategy. It leverages GPU parallelism to accelerate the large-scale computation of a node similarity matrix of interest. Our experimentation showed a relevant improvement in terms of runtime when processing is executed on GPU, in comparison to the traditional CPU computing. Briefly, our method has proven to be an effective solution for the alignment of large networks, especially with dense similarity matrices, thus proving to be an ideal candidate for use in real-world study cases.

BACKGROUND: In bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which incorporates a specific entity (i.e., node) and only one type of link (i.e., edge). However, real-world biological systems consisting of biological objects belonging to heterogeneous entities, and these operate and influence each other in multiple contexts, simultaneously. Usually, node similarities are investigated to assess the relatedness between biological objects in a network of interest, and node embeddings are widely used for studying novel interaction from a topological point of view. About that, the state-of-the-art presents several methods for evaluating the node similarity inside a given network, but methodologies able to evaluate similarities between pairs of nodes belonging to different networks are missing. The latter are crucial for studies that relate different biological networks, e.g., for Network Alignment or to evaluate the possible evolution of the interactions of a little-known network on the basis of a well-known one. Existing methods are ineffective in evaluating nodes outside their structure, even more so in the context of multilayer networks, in which the topic still exploits approaches adapted from static networks. In this paper, we presented pyMulSim, a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks. It uses a Graph Isomorphism Network (GIN) for the representative learning of node features, that uses for processing the embeddings and computing the similarities between the pairs of nodes of different multilayer networks. RESULTS: Our experimentation investigated the performance of our method. Results show that our method effectively evaluates the similarities between the biological objects of a source multilayer network to a target one, based on the analysis of the node embeddings. Results have been also assessed for different noise levels, also through statistical significance analyses properly performed for this purpose. CONCLUSIONS: PyMulSim is a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks, by using a GIN for learning node embeddings. It has been evaluated both in terms of performance and validity, reporting a high degree of reliability.

Healthcare data breaches are a growing problem that seriously threatens patient privacy, the reputation and trustworthiness of both public and private healthcare organizations. The aim of this paper is to elucidate the severity of healthcare data breaches, their potential impact on patient privacy and healthcare organizations, providing a predictive data breaches transformer conceptual architecture that can monitoring users actions that can result in possible system security violation and consequently in data breaches. At this regard, we introduce the description of a concept architecture for implementing a predictive data breaches transformer highlighting weakness and strengthens, and in the same time how the adoption of a predictive system can significantly limit the risk of the onset of possible data breaches by making the operator more aware in carrying out his activity in the processing of each type of data including personal health data.

Serverless computing is an emerging cloud service for executing distributed applications on cloud architecture. The possibility of performing functions without the need to manage any type of infrastructure has made this methodology particularly adopted in several fields, e.g., data processing and above all in parallel computing. The processing of large-scale genomic data needs many computational resources, resulting highly time-consuming. Therefore, the need of higher computing capabilities has translated into the increasing use of this technology. In this paper, we present a method for modelling and executing customized pipelines in serverless computing. We applied this one to the transcript-level expression analysis of samples from RNA sequencing (RNA-seq), by focusing on the most computationally expensive step: the mapping of reads to a reference genome. Our method has been implemented as an Amazon Web Services (AWS) Lambda function, that is deployed within our own serverless architecture. The parallel instances invoked in AWS Lambda are with negligible latencies, being managed by the provider, therefore, the average computational time are similar among experiments on similar samples. We denoted a relevant advantage in running time, by measuring an improvement up to 79.84% and 90.10% on the concurrent analysis of 10 samples, compared to the local environments having the following specifications: CPU 3.8 GHz 8 vcores and CPU 3.8 GHz 16 vcores, respectively.

BACKGROUND: Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. RESULTS: We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. CONCLUSIONS: DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution.

In the real-world systems, the interactions between objects (e.g., molecules) are generally represented through the dynamic networks, based on a graph-model that evolve over the time. Network alignment allows evaluating different networks in terms of homology and topology. It is a method to map nodes from different networks, in order to match the same entities. For instance, we can assume that the topological similarity between the regions of two given networks corresponds to their functionality, e.g., in terms of biological process. Therefore, the alignment between the biological networks of two species may be useful to transfer the knowledge from the simplest (e.g., mouse) to the more complex (e.g., human).In this paper, we present DANTE (DynAmic Networks alignment based on Temporal Embeddings), a solution for the alignment of dynamic networks, based on temporal embeddings. DANTE takes into account the similarity between the nodes based on their evolution and relationships between one time point and the subsequent, for all ones in the networks. Our temporal embeddings concern a set of vectors representing the topological similarity between the nodes of two given networks, by representing each node as a word on the Skip-Gram model. In addition, we extend the embeddings process to the dynamic networks, in order to evaluate the similarity between two dynamic networks. DANTE applies an iterative process for maximizing globally the match score between the pair of nodes.DANTE is freely available on https://github.com/pietrocinaglia/dante.

Serverless is a computing model where the infrastructure orchestration is managed by the service provider. Amazon Web Services (AWS) offers a serverless computing service named Lambda (or AWS Lambda). It allows deploying a function on AWS by executing this one on a serverless environment. The analysis of RNA-Seq data needs a large amount of computational resources, and it is really time-consuming. Therefore, the solutions based on serverless computing could offer a tangible benefit, being well-prepared to the parallelization. In this paper, we proposed a serverless solution for RNA-Seq data analysis, by focusing the attention on the mapping/alignment of sequencing reads to a reference genome, in that it is the more computationally expensive step within an RNA-Seq pipeline. We deployed our solution on an in-house serverless environment, useful to overcome the limits of a traditional AWS Lambda configuration. The tests have been conducted on 16 Paired-end sequencing samples, by using the Ensembl’s GRCh38 (release 84, Homo sapiens) as reference genome. Furthermore, we also estimated the probable time needed to map 50, 100, 250, 500, and 1000 samples based on our 16 ones. The proposed solution has been compared with local analysis performed by using two different workstations. We demonstrate an obvious advantage for our use-case. Results showed that a serverless solution is only suitable for highly parallel computations or for a lot of number of independent atomic operations, while it is not recommended for a single atomic operation that requires a lot amount of CPU and/or memory.

Dynamic biological networks model changes in the network topology over time. However, often the topologies of these networks are not available at specific time points. Existing algorithms for studying dynamic networks often ignore this problem and focus only on the time points at which experimental data is available. In this paper, we develop a novel alignment based network construction algorithm, ANCA, that constructs the dynamic networks at the missing time points by exploiting the information from a reference dynamic network. Our experiments on synthetic and real networks demonstrate that ANCA predicts the missing target networks accurately, and scales to large-scale biological networks in practical time. Our analysis of an E. coli protein-protein interaction network shows that ANCA successfully identifies key temporal changes in the biological networks. Our analysis also suggests that by focusing on the topological differences in the network, our method can be used to find important genes and temporal functional changes in the biological networks.

Extracting morphological features from DICOM images is useful to obtain numerical anatomic values for population-wide studies. Currently software tools on medical devices are able to extract some parameters that can indicate the presence of diseases. Nevertheless, there still is a lot of not exploited information contained in images which can be useful for research as well as to characterize human behavior. For instance, measures for lung volume compared with reference data sets can be studied starting from clinical images.

Electroencephalography (EEG) is a technique for the acquisition of electrical brain signals. In recent years the increase of information acquired from signal analysis has generated a large amount of data; therefore, the development of tools for analysis has become necessary. In this paper, the mEEG prototype for EEG data managing is presented. It offers a user-friendly communication solution to exchange data between physicians and biomedical engineers. Features can be used for: (i) perform a fast diagnoses; (ii) show reports about clinical information; (iii) store and retrieve neurological data.