0000000000046687
AUTHOR
Cristina Serrao
Discriminating graph pattern mining from gene expression data
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 …
Data Sources and Models
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…
(Discriminative) Pattern Discovery on Biological Networks
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 …
Exceptional Pattern Discovery
This chapter is devoted to a discussion on exceptional pattern discovery, namely on scenarios, contexts, and techniques concerning the mining of patterns which are so rare or so frequent to be considered as exceptional and, then, of interest for an expert to shed lights on the domain. Frequent patterns have found broad applications in areas like association rule mining, indexing, and clustering [1, 20, 23]. The application of frequent patterns in classification also achieved some success in the classification of relational data [6, 13, 14, 19, 25], text [15], and graphs [7]. The part is organized as follows. First, the frequent pattern mining on classical datasets is presented. This is not …
Discovering discriminative graph patterns from gene expression data
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…
Problems and Techniques
When biological networks are considered, the extraction of interesting knowledge often involves subgraphs isomorphism check that is known to be NP-complete. For this reason, many approaches try to simplify the problem under consideration by considering structures simpler than graphs, such as trees or paths. Furthermore, the number of existing approximate techniques is notably greater than the number of exact methods. In this chapter, we provide an overview of three important problems defined on biological networks: network alignment, network clustering, and motifs extraction from biological networks. For each of these problems, we also describe some of the most important techniques proposed…
Discriminative pattern discovery for the characterization of different network populations
Abstract Motivation An interesting problem is to study how gene co-expression varies in two different populations, associated with healthy and unhealthy individuals, respectively. To this aim, two important aspects should be taken into account: (i) in some cases, pairs/groups of genes show collaborative attitudes, emerging in the study of disorders and diseases; (ii) information coming from each single individual may be crucial to capture specific details, at the basis of complex cellular mechanisms; therefore, it is important avoiding to miss potentially powerful information, associated with the single samples. Results Here, a novel approach is proposed, such that two different input popul…