Search results for "Biological networks"
showing 10 items of 11 documents
Discovering discriminative graph patterns from gene expression data
2016
We consider the problem of mining gene expression data in order to single out interesting features characterizing healthy/unhealthy samples of an input dataset. We present an approach based on a network model of the input gene expression data, where there is a labelled graph for each sample. To the best of our knowledge, this is the first attempt to build a different graph for each sample and, then, to have a database of graphs for representing a sample set. Our main goal is that of singling out interesting differences between healthy and unhealthy samples, through the extraction of "discriminative patterns" among graphs belonging to the two different sample sets. Differently from the other…
Textual data compression in computational biology: Algorithmic techniques
2012
Abstract In a recent review [R. Giancarlo, D. Scaturro, F. Utro, Textual data compression in computational biology: a synopsis, Bioinformatics 25 (2009) 1575–1586] the first systematic organization and presentation of the impact of textual data compression for the analysis of biological data has been given. Its main focus was on a systematic presentation of the key areas of bioinformatics and computational biology where compression has been used together with a technical presentation of how well-known notions from information theory have been adapted to successfully work on biological data. Rather surprisingly, the use of data compression is pervasive in computational biology. Starting from…
A Coclustering Approach for Mining Large Protein-Protein Interaction Networks
2012
Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks. Indeed, methods returning high accurate results are often able to cover only small parts of the input PPI network, especially when low-characterized networks are considered. We present a coclustering-based technique able to generate both overlapping and nonove…
Searching for repetitions in biological networks: methods, resources and tools
2013
We present here a compact overview of the data, models and methods proposed for the analysis of biological networks based on the search for significant repetitions. In particular, we concentrate on three problems widely studied in the literature: ‘network alignment’, ‘network querying’ and ‘network motif extraction’. We provide (i) details of the experimental techniques used to obtain the main types of interaction data, (ii) descriptions of the models and approaches introduced to solve such problems and (iii) pointers to both the available databases and software tools. The intent is to lay out a useful roadmap for identifying suitable strategies to analyse cellular data, possibly based on t…
DIAMIN: a software library for the distributed analysis of large-scale molecular interaction networks
2022
AbstractBackgroundHuge amounts of molecular interaction data are continuously produced and stored in public databases. Although many bioinformatics tools have been proposed in the literature for their analysis, based on their modeling through different types of biological networks, several problems still remain unsolved when the problem turns on a large scale.ResultsWe propose , that is, a high-level software library to facilitate the development of applications for the efficient analysis of large-scale molecular interaction networks. relies on distributed computing, and it is implemented in Java upon the framework Apache Spark. It delivers a set of functionalities implementing different ta…
(Discriminative) Pattern Discovery on Biological Networks
2017
This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example …
Knowledge Extraction from Biological and Social Graphs
2022
Many problems from the real life deal with the generation of enormous, varied, dynamic, and interconnected datasets coming from different and heterogeneous sources. This PhD Thesis focuses on the proposal of novel knowledge extraction techniques from graphs, mainly based on Big Data methodologies. Two application contexts are considered: Biological and Medical data, with the final aim of identifying biomarkers for diagnosis, treatment, prognosis, and prevention of diseases. Social data, for the optimization of advertising campaigns, the comparison of user profiles, and neighborhood analysis.
Algorithms for Graph and Network Analysis: Graph Alignment
2019
In this article we discuss the problem of graph alignment, which has been longly referred to for the purpose of analyzing and comparing biological networks. In particular, we describe different facets of graph alignment, according to the number of input networks, the fixed output objective, the possible heterogeneity of input data. Accordingly, we will discuss pairwise and multiple alignment, global and local alignment, etc. Moreover, we provide a comprehensive overview of the algorithms and techniques proposed in the literature to solve each of the specific considered types of graph alignment. In order to make the material presented here complete and useful to guide the reader in the use o…
Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods
2014
Abstract Motivation: Protein–protein interaction (PPI) networks are powerful models to represent the pairwise protein interactions of the organisms. Clustering PPI networks can be useful for isolating groups of interacting proteins that participate in the same biological processes or that perform together specific biological functions. Evolutionary orthologies can be inferred this way, as well as functions and properties of yet uncharacterized proteins. Results: We present an overview of the main state-of-the-art clustering methods that have been applied to PPI networks over the past decade. We distinguish five specific categories of approaches, describe and compare their main features and …
Topological ranks reveal functional knowledge encoded in biological networks: a comparative analysis
2022
Abstract Motivation Biological networks topology yields important insights into biological function, occurrence of diseases and drug design. In the last few years, different types of topological measures have been introduced and applied to infer the biological relevance of network components/interactions, according to their position within the network structure. Although comparisons of such measures have been previously proposed, to what extent the topology per se may lead to the extraction of novel biological knowledge has never been critically examined nor formalized in the literature. Results We present a comparative analysis of nine outstanding topological measures, based on compact vie…