Extracting Dense and Connected Subgraphs in Dual Networks by Network Alignment

The use of network based approaches to model and analyse large datasets is currently a growing research field. For instance in biology and medicine, networks are used to model interactions among biological molecules as well as relations among patients. Similarly, data coming from social networks can be trivially modelled by using graphs. More recently, the use of dual networks gained the attention of researchers. A dual network model uses a pair of graphs to model a scenario in which one of the two graphs is usually unweighted (a network representing physical associations among nodes) while the other one is edge-weighted (a network representing conceptual associations among nodes). In this paper we focus on the problem of finding the Densest Connected sub-graph (DCS) having the largest density in the conceptual network which is also connected in the physical network. The problem is relevant but also computationally hard, therefore the need for introducing of novel algorithms arises. We formalise the problem and then we map DCS into a graph alignment problem. Then we propose a possible solution. A set of experiments is also presented to support our approach

vocal signal analysis in patients affected by multiple sclerosis

Abstract Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient's quality of life

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks

BACKGROUND: Protein inter-residue contact maps provide a translation and rotation invariant topological representation of a protein. They can be used as an intermediary step in protein structure predictions. However, the prediction of contact maps represents an unbalanced problem as far fewer examples of contacts than non-contacts exist in a protein structure. In this study we explore the possibility of completely eliminating the unbalanced nature of the contact map prediction problem by predicting real-value distances between residues. Predicting full inter-residue distance maps and applying them in protein structure predictions has been relatively unexplored in the past. RESULTS: We initially demonstrate that the use of native-like distance maps is able to reproduce 3D structures almost identical to the targets, giving an average RMSD of 0.5Å. In addition, the corrupted physical maps with an introduced random error of ±6Å are able to reconstruct the targets within an average RMSD of 2Å. After demonstrating the reconstruction potential of distance maps, we develop two classes of predictors using two-dimensional recursive neural networks: an ab initio predictor that relies only on the protein sequence and evolutionary information, and a template-based predictor in which additional structural homology information is provided. We find that the ab initio predictor is able to reproduce distances with an RMSD of 6Å, regardless of the evolutionary content provided. Furthermore, we show that the template-based predictor exploits both sequence and structure information even in cases of dubious homology and outperforms the best template hit with a clear margin of up to 3.7Å. Lastly, we demonstrate the ability of the two predictors to reconstruct the CASP9 targets shorter than 200 residues producing the results similar to the state of the machine learning art approach implemented in the Distill server. CONCLUSIONS: The methodology presented here, if complemented by more complex reconstruction protocols, can represent a possible path to improve machine learning algorithms for 3D protein structure prediction. Moreover, it can be used as an intermediary step in protein structure predictions either on its own or complemented by NMR restraints

On the analysis of biomedical signals for disease classification

The analysis of biomedical signals and images is relevant for early diagnosis, detection and treatment of diseases. It represents the first step in the proper management of pathological conditions. Therefore, it is essential to support clinical practice during the diagnosis process by extracting relevant information and by classifying different diseases. This contribution outlines the methodologies of the most frequently used analysis techniques in biomedicine and their applications. The aim is to report about typical biosignals and bioimages and their analysis to enhance the importance of signal processing in the study and classification of specific diseases

A Novel Algorithm for Local Network Alignment Based on Network Embedding

Networks are widely used in bioinformatics and biomedicine to represent associations across a large class of biological entities. Network alignment refers to the set of approaches that aim to reveal similarities among networks. Local Network Alignment (LNA) algorithms find (relatively small) local regions of similarity between two or more networks. Such algorithms are in general based on a set of seed nodes that are used to build the alignment incrementally. A large fraction of LNA algorithms uses a set of vertices based on context information as seed nodes, even if this may cause a bias or a data-circularity problem. Moreover, using topology information to choose seed nodes improves overall alignment. Finally, similarities among nodes can be identified by network embedding methods (or representation learning). Given there are two networks, we propose to use network embedding to capture structural similarity among nodes, which can also be used to improve LNA effectiveness. We present an algorithm and experimental tests on real and syntactic graph data to find LNAs

Associating genomics and clinical information by means of semantic based ranking

Relating genomic data with clinical and disease information is a new challenge for life sciences research. High performance computational platforms allow huge quantity of biological data production with new technologies (e.g. Next Generation Sequencing techniques). Nowadays, genomic ontologies describing genes and functions, as well as databases containing diseases groups, are available. We focus on the problem of enriching genomic datasets containing miRNA genes by adding related disease information. The enrichment is performed by using ontologies to find genes-to-diseases associations. Ontologies are used to describe molecular genomic processes and functions, as well as disease classes and experimental details. International Classification of Diseases (ICD) is used for the classification of diseases and clinical information. Diseases are ranked by using a Google Page Rank based algorithm. An application tool called Surf App! has been coded and developed in R and tested on a neurological disease dataset

Geomedica: Managing and querying clinical data distributions on geographical database systems

AbstractGeographical databases are a significant and mature tool, useful in many application areas thanks to the spread of new positioning and mapping technologies. Geographical functionalities can be added to existing applications, from land management to water and electricity control systems. The use of geographical information applications greatly improves data interpretation, thus helping users in making better decisions. Further improvements can be obtained by using more sophisticated tools (e.g. On Line Analytical Processing and Data Mining techniques) to highlight interesting and previously unknown relations on spatio-temporal data, which can help in a better understanding of data.In this paper we report the experience of using GIS technologies to analyze clinical data containing health information about a large population. Clinical data have been geocoded by associating tuples related to some geographical position with the coordinates of a map and then analyzed and queried using both SQL-like languages and a graphical user interface. Several experiments have been performed using data related to an italian district which have been furnished by an association of family doctors and patients. Test queries performed on the available dataset were able to correctly correlate health data about patients with geographical features (e.g. points of interest, boundaries, coastlines vectors) and to visualize diseases geographical distributions on a map