Corrado Loglisci

Computational annotation of UTR cis-regulatory modules through Frequent Pattern Mining

BackgroundMany studies report about detection and functional characterization of cis-regulatory motifs in untranslated regions (UTRs) of mRNAs but little is known about the nature and functional role of their distribution. To address this issue we have developed a computational approach based on the use of data mining techniques. The idea is that of mining frequent combinations of translation regulatory motifs, since their significant co-occurrences could reveal functional relationships important for the post-transcriptional control of gene expression. The experimentation has been focused on targeted mitochondrial transcripts to elucidate the role of translational control in mitochondrial biogenesis and function.ResultsThe analysis is based on a two-stepped procedure using a sequential pattern mining algorithm. The first step searches for frequent patterns (FPs) of motifs without taking into account their spatial displacement. In the second step, frequent sequential patterns (FSPs) of spaced motifs are generated by taking into account the conservation of spacers between each ordered pair of co-occurring motifs. The algorithm makes no assumption on the relation among motifs and on the number of motifs involved in a pattern. Different FSPs can be found depending on different combinations of two parameters, i.e. the threshold of the minimum percentage of sequences supporting the pattern, and the granularity of spacer discretization. Results can be retrieved at the UTRminer web site: http://utrminer.ba.itb.cnr.it/. The discovered FPs of motifs amount to 216 in the overall dataset and to 140 in the human subset. For each FP, the system provides information on the discovered FSPs, if any. A variety of search options help users in browsing the web resource. The list of sequence IDs supporting each pattern can be used for the retrieval of information from the UTRminer database.ConclusionComputational prediction of structural properties of regulatory sequences is not trivial. The presented data mining approach is able to overcome some limits observed in other competitive tools. Preliminary results on UTR sequences from nuclear transcripts targeting mitochondria are promising and lead us to be confident on the effectiveness of the approach for future developments.

Mining generalized association rules on biomedical literature

The discovery of new and potentially meaningful relationships between concepts in the biomedical literature has attracted the attention of a lot of researchers in text mining. The main motivation is found in the increasing availability of the biomedical literature which makes it difficult for researchers in biomedicine to keep up with research progresses without the help of automatic knowledge discovery techniques. More than 14 million abstracts of this literature are contained in the Medline collection and are available online. In this paper we present the application of an association rule mining method to Medline abstracts in order to detect associations between concepts as indication of the existence of a biomedical relation among them. The discovery process fully exploits the MeSH (Medical Subject Headings) taxonomy, that is, a set of hierarchically related biomedical terms which permits to express associations at different levels of abstraction (generalized association rules). We report experimental results on a collection of abstracts obtained by querying Medline on a specific disease and we show the effectiveness of some filtering and browsing techniques designed to manage the huge amount of generalized associations that may be generated on real data.

Relational mining for discovering changes in evolving networks

Networks are data structures more and more frequently used for modeling interactions in social and biological phenomena, as well as between various types of devices, tools and machines. They can be either static or dynamic, dependently on whether the modeled interactions are fixed or changeable over time. Static networks have been extensively investigated in data mining, while fewer studies have focused on dynamic networks and how to discover complex patterns in large, evolving networks. In this paper we focus on the task of discovering changes in evolving networks and we overcome some limits of existing methods (i) by resorting to a relational approach for representing networks characterized by heterogeneous nodes and/or heterogeneous relationships, and (ii) by proposing a novel algorithm for discovering changes in the structure of a dynamic network over time. Experimental results and comparisons with existing approaches on real-world datasets prove the effectiveness and efficiency of the proposed solution and provide some insights on the effect of some parameters in discovering and modeling the evolution of the whole network, or a subpart of it.

Leveraging temporal autocorrelation of historical data for improving accuracy in network regression

Temporal data describe processes and phenomena that evolve over time. In many real-world applications temporal data are characterized by temporal autocorrelation, which expresses the dependence of time-stamped data over a certain a time lag. Often such processes and phenomena are characterized by evolving complex entities, which we can represent with evolving networks of data. In this scenario, a task that deserves attention is regression inference in temporal network data. In this paper, we investigate how to improve the predictive inference on network data by accommodating temporal autocorrelation of the historical data in the learning process of the prediction models. Historical data is a type of temporal data where most part of the elements has been already stored. In practice, we study how to explicitly consider the influence of data of a network observed in the past, to enhance the prediction on the same network observed at the present. The proposed approach relies on a model ensemble built with individual predictors learned on historical network data. The predictors are trained from summary networks, which synthesize the effect of the autocorrelation in distinct sequences of network observations. Summary networks are identified with a sliding window model. Finally, the model ensemble combines together the predictors with a weighting schema, which reflects the degree of influence of a predictor with respect to the network observed at the present. So, we aim at accommodating the temporal autocorrelation both in the data and in the prediction model. Empirical evaluation demonstrates that the proposed approach can boost regression performance in real-world network data.

Segmentation of Evolving Complex Data and Generation of Models

The problem of time-series segmentation has been widely discussed and it has been successfully applied in a variety of areas including computational genomics, telecommunications and process monitoring. Nevertheless not many techniques have been devised to deal with multidimensional evolving data describing complex objects. Moreover, in many applications the resulting segments have not a description understandable to the user, and this is exacerbated in the applications with complex data. Our contribute aims to propose an algorithmic framework to segment multidimensional evolving data or multidimensional time-series and to resort to an ILP system to generate characterizations of segments close to the user. The application and the results to the real-world data are reported

Collective regression for handling autocorrelation of network data in a transductive setting

Sensor networks, communication and financial networks, web and social networks are becoming increasingly important in our day-to-day life. They contain entities which may interact with one another. These interactions are often characterized by a form of autocorrelation, where the value of an attribute at a given entity depends on the values at the entities it is interacting with. In this situation, the collective inference paradigm offers a unique opportunity to improve the performance of predictive models on network data, as interacting instances are labeled simultaneously by dealing with autocorrelation. Several recent works have shown that collective inference is a powerful paradigm, but it is mainly developed with a fully-labeled training network. In contrast, while it may be cheap to acquire the network topology, it may be costly to acquire node labels for training. In this paper, we examine how to explicitly consider autocorrelation when performing regression inference within network data. In particular, we study the transduction of collective regression when a sparsely labeled network is a common situation. We present an algorithm, called CORENA (COllective REgression in Network dAta), to assign a numeric label to each instance in the network. In particular, we iteratively augment the representation of each instance with instances sharing correlated representations across the network. In this way, the proposed learning model is able to capture autocorrelations of labels over a group of related instances and feed-back the more reliable labels predicted by the transduction in the labeled network. Empirical studies demonstrate that the proposed approach can boost regression performances in several spatial and social tasks.

Toward Geographic Information Harvesting: Extraction of Spatial Relational Facts from Web Documents

This paper faces the problem of harvesting geographic information from Web documents, specifically, extracting facts on spatial relations among geographic places. The motivation is twofold. First, researchers on Spatial Data Mining often assume that spatial data are already available, thanks to current GIS and positioning technologies. Nevertheless, this is not applicable to the case of spatial information embedded in data without an explicit spatial modeling, such as documents. Second, despite the huge amount of Web documents conveying useful geographic information, there is not much work on how to harvest spatial data from these documents. The problem is particularly challenging because of the lack of annotated documents, which prevents the application of supervised learning techniques. In this paper, we propose to harvest facts on geographic places through an unsupervised approach which recognizes spatial relations among geographic places without supposing the availability of annotated documents. The proposed approach is based on the combined use of a spatial ontology and a prototype-based classifier. A case study on topological and directional relations is reported and commented.

Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies

Association rules (AR) are a class of patterns which describe regularities in a set of transactions. When items of transactions are organized in a taxonomy, AR can be associated with a level of the taxonomy since they contain only items at that level. A drawback of multiple level AR mining is represented by the generation of redundant rules which do not add further information to that expressed by other rules. In this paper, a method for the discovery of non-redundant multiple level AR is proposed. It follows the usual two-stepped procedure for AR mining and it prunes redundancies in each step. In the first step, redundancies are removed by resorting to the notion of multiple level closed frequent itemsets , while in the second step, pruning is based on an extension of the notion of minimal rules . The proposed technique has been applied to a real case of analysis of textual data. An empirical comparison with the Apriori algorithm proves the advantages of the proposed method in terms of both time-performance and redundancy reduction.

Novelty Detection from Evolving Complex Data Streams with Time Windows

Novelty detection in data stream mining denotes the identification of new or unknown situations in a stream of data elements flowing continuously in at rapid rate. This work is a first attempt of investigating the anomaly detection task in the (multi-)relational data mining. By defining a data block as the collection of complex data which periodically flow in the stream, a relational pattern base is incrementally maintained each time a new data block flows in. For each pattern, the time consecutive support values collected over the data blocks of a time window are clustered, clusters are then used to identify the novelty patterns which describe a change in the evolving pattern base. An application to the problem of detecting novelties in an Internet packet stream is discussed.

Hierarchical and overlapping co-clustering of mRNA: iRNA interactions

microRNAs (miRNAs) are an important class of regulatory factors controlling gene expressions at post-transcriptional level. Studies on interactions between different miRNAs and their target genes are of utmost importance to understand the role of miRNAs in the control of biological processes. This paper contributes to these studies by proposing a method for the extraction of co-clusters of miRNAs and messenger RNAs (mRNAs). Different from several already available co-clustering algorithms, our approach efficiently extracts a set of possibly overlapping, exhaustive and hierarchically organized co-clusters. The algorithm is well-suited for the task at hand since: i) mRNAs and miRNAs can be involved in different regulatory networks that may or may not be co-active under some conditions, ii) exhaustive co-clusters guarantee that possible co-regulations are not lost, iii) hierarchical browsing of co-clusters facilitates biologists in the interpretation of results. Results on synthetic and on real human miRNA:mRNA data show the effectiveness of the approach.