Search results for "Biological network"
showing 10 items of 32 documents
Discriminating graph pattern mining from gene expression data
2016
We consider the problem of mining gene expression data in order to single out interesting features that characterize healthy/unhealthy samples of an input dataset. We present and 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. Out main goal is that of singling out interesting differences between healthy and unhealthy samples, through the extraction of "discriminating patterns" among graphs belonging to the two different sample sets. Differently from the …
Towards patient stratification and treatment in the autoimmune disease lupus erythematosus using a systems pharmacology approach
2015
Drug development in Systemic Lupus Erythematosus (SLE) has been hindered by poor translation from successful preclinical experiments to clinical efficacy. This lack of success has been attributed to the high heterogeneity of SLE patients and to the lack of understanding of disease physiopathology. Modelling approaches could be useful for supporting the identification of targets, biomarkers and patient subpopulations with differential response to drugs. However, the use of traditional quantitative models based on differential equations is not justifiable in a sparse data situation. Boolean networks models are less demanding on the required data to be implemented and can provide insights into…
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…
An Integrative Framework for the Construction of Big Functional Networks
2018
We present a methodology for biological data integration, aiming at building and analysing large functional networks which model complex genotype-phenotype associations. A functional network is a graph where nodes represent cellular components (e.g., genes, proteins, mRNA, etc.) and edges represent associations among such molecules. Different types of components may cohesist in the same network, and associations may be related to physical[biochemical interactions or functional/phenotipic relationships. Due to both the large amount of involved information and the computational complexity typical of the problems in this domain, the proposed framework is based on big data technologies (Spark a…
Editorial: Protein Interaction Networks in Health and Disease
2016
The identification and annotation of protein-protein interactions (PPIs) is of great importance in systems biology. Big data produced from experimental or computational approaches allow not only the construction of large protein interaction maps but also expand our knowledge on how proteins build up molecular complexes to perform sophisticated tasks inside a cell. However, if we want to accurately understand the functionality of these complexes, we need to go beyond the simple identification of PPIs. We need to know when and where an interaction happens in the cell and also understand the flow of information through a protein interaction network. Another perspective of the research on PPI n…
New Trends in Graph Mining
2010
Searching for repeated features characterizing biological data is fundamental in computational biology. When biological networks are under analysis, the presence of repeated modules across the same network (or several distinct ones) is shown to be very relevant. Indeed, several studies prove that biological networks can be often understood in terms of coalitions of basic repeated building blocks, often referred to as network motifs.This work provides a review of the main techniques proposed for motif extraction from biological networks. In particular, main intrinsic difficulties related to the problem are pointed out, along with solutions proposed in the literature to overcome them. Open ch…
Data Sources and Models
2017
Biological networks rely on the storage and retrieval of data associated to the physical interactions and/or functional relationships among different actors. In particular, the attention may be on the interactions among cellular components, such as proteins, genes, RNA, or for example on phenotype–genotype associations. Data from which biological networks are built are usually stored in public databases, and we provide here a brief summary of the main types of both data and associations, publicly available. Moreover, we also explain how it is possible to construct suitable network models from these associations, focusing on protein–protein interaction networks, gene–disease networks and net…
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…