The emergence of diseases and drug-induced perturbations are oftentimes the cause of biological pathway deregulations. Identifying differentially expressed subpathways in organism-level networks of signaling pathways can be a computationally intensive undertaking, due to their complexity. In this direction, we present a subpathway analysis method which refines organism-level networks via a two step-approach. The method first constructs a core-network of differentially expressed genes and subsequently includes a set of topologically significant non-differentially expressed genes, that both exhibit correlated expression levels with their neighbors and facilitate the signal propagation within the core-network. The refined network is then searched for differentially expressed subpathways using a plethora of subpathway identification methods and checked for enrichment in functional terms such as drugs and diseases. The approach assesses the differential expression of the subpathways using a consensus approach, by detecting even weak signals of differential expression, while accounting for correlations which arise in gene expression data.

DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customized framework with a broad range of operation modes at all stages of the subpathway analysis, enabling so a case-specific approach. The operation modes include pathway network construction and processing, subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render DEsubs a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level drug targets and biomarkers for complex diseases. AVAILABILITY AND IMPLEMENTATION: DEsubs is implemented as an R package following Bioconductor guidelines: http://bioconductor.org/packages/DEsubs/ CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

MOTIVATION: In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific 'active parts' of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical 'themes'-in the form of enriched biologically relevant microRNA-mediated subpathways-that determine the functionality of signaling networks across time. RESULTS: To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways. AVAILABILITY AND IMPLEMENTATION: CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we propose a novel approach to address the frequent item set hiding problem, by formulating it as an integer linear program (ILP). The solution of the ILP points out the transactions that need to be sanitized in order to achieve the hiding of the sensitive frequent item sets, while the impact on other non-sensitive item sets is minimized. We present a novel heuristic approach to calculate the coefficients of the objective function of the ILP, while at the same time we minimize the side effects introduced by the hiding process. We also propose a sanitization algorithm that performs the hiding on the selected transactions. Finally, we evaluate the proposed method on real datasets and we compare the results of the newly proposed method with those of other state of the art approaches.

Over the last years, the advent of social networks has changed the way of human communication giving users the ability to express their thoughts and opinions. In this paper, we present a work on analyzing human communication and interaction in Twitter. The aim is to get indicative factors about user's behavior as well as public stance and attitude towards various events around the globe. The methodology initially analyzes users' tweets and determines their emotional content based on Ekman emotional scale. Then, user's characteristics and behavior in Twitter are analyzed and their influence in the network is calculated. Based on tweets emotional content as well as user's influence, the conversation emotional graphs are developed to model and represent user's emotional interactions. Furthermore, we introduce a prediction method using machine learning techniques in order to discover the changes of topic emotional content during users' discussion.

MOTIVATION: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem of missing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. RESULTS: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a two-step algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. AVAILABILITY AND IMPLEMENTATION: Datasets and codes are freely available on the Web at http://prlab.ceid.upatras.gr/EnsembleGASVR/dataset-codes.zip. All the required information about the article is available through http://prlab.ceid.upatras.gr/EnsembleGASVR/site.html.

With the increasing use of web and Service Oriented Systems, web-services have become a widely adopted technology. Web services repositories are growing fast, creating the need for advanced tools for organizing and indexing them. Clustering web services, usually represented by Web Service Description Language (WSDL) documents, enables the web service search engines and users to organize and process large web service repositories in groups with similar functionality and characteristics. In this paper, we propose a novel technique of clustering WSDL documents. The proposed method considers web services as categorical data and each service is described by a set of values extracted from the content and structure of its description file and as quality measure of clustering is defined the mutual information of the clusters and their values. We describe the way to represent web services as categorical data and how to cluster them by using LIMBO algorithm, minimizing at the same time the information loss in features values. In experimental evaluation, our approach outperforms in terms of F-Measure the approaches which use alternative similarity measures and methods for clustering WSDL documents.

Positioning capabilities offered in modern mobile devices enable usage of location-based services. Privacy and security is of great importance for related applications. We present a framework that allows conducting research on anonymity techniques in a real-life environment using smartphones. The proposed solution also includes logging mechanisms that facilitate positioning research dataset development in open format. To present the capabilities of the solution, we deliver the concept of K-anonymity to protect mobile users that issue queries to location-based services. Experimental evaluation of the solution includes development of real-life logging dataset using smartphones by volunteers. Different flavours of anonymity algorithms are easy to be included and tested. The solution has received encouraging feedback and successfully assists the researchers of location based services to experiment, validate and develop their techniques in real life environment.

Nowadays, autoimmune diseases are among the leading causes of death for a remarkable number of patients all around the world. Recent studies have witnessed that the epidemiological indices for a specific disease can vary according to ethnic and geographical parameters. As a result, the genetic epidemiology of autoimmune diseases is a major matter of study for the worldwide scientific community. We have previously reported the development of dAUTObase (www.dAUTObase.org), a database recording solely epidemiological data of autoimmune diseases in various populations around the globe. Here, we present an important upgrade of the dAUTObase system focused on the development of new data visualization tools oriented to further assist the effective data querying and the mining process.

Nowadays, people frequently use search engines in order to find the information they need on the web. However, usually web search engines return web page references in a global ranking making it difficult to the users to browse different topics captured in the result set and thus making it difficult to find quickly the desired web pages. There is need for special computational systems, that will discover knowledge in these web search results providing the user with the possibility to browse different topics contained in a given result set. In this paper, we focus on the problem of determining different thematic groups on web search engine results that existing web search engines provide. We propose a novel system that exploits a set of reformulation strategies so as to help users gain more relevant results to their desired query. It additionally tries to discover among the result set different topic groups, according to the various meanings of the provided query. The proposed method utilizes a number of semantic annotation techniques using Knowledge Bases, like Word Net and Wikipedia, in order to perceive the different senses of each query term. Finally, the method annotates the extracted topics using information derived from the clusters and presents them to the end user.

The explosive growth in the size and use of the World Wide Web continuously creates new great challenges and needs. One such need is dealing with click fraud, which aims at increasing clicks on certain ads and thus the profit of the websites which display them. In this work, we extend the concept of click fraud and redefine it as any pattern of clicks whose goal is to alternate the normal operation of a website in order to produce specific results. An indication of a click fraud may be a burst of clicks that can be simulated by an automated program or script. We deal with the problem of efficient real-time Click Fraud detection utilizing advanced data structures and exploiting their advantages concerning space and time required.

In this paper we discuss the integration of QoS-awa research and personalization algorithms in order to discover effectual Web Services for the case of mobile web users. We present a number of novel ranking algorithms specially designed for mobile web architectures. To validate and evaluate the proposed algorithms we developed a fully working prototype for mobile-PDA devices. The prototype enables PDA users to access electronic shops and to retrieve information about their products, while going shopping. Comparative experimental results have shown encouraging results that prove m-scroutz to be effective.

A multirepeat in a string is a substring (factor) that appears a predefined number of times. A multirepeat is maximal if it cannot be extended either to the right or to the left and produce a multirepeat. In this paper, we present algorithms for two different versions of the problem of finding maximal multirepeats in a set of strings. In the case of arbitrary gaps, we propose an algorithm with O ( σN2n + α ) time complexity. When the gap is bounded in a small range c , we propose an algorithm with O (( c2 + σ2 ) mN2n log( Nn ) + α ) time complexity. Here, N is the number of strings, n the mean length of each string, m the multiplicity of the multirepeat and α the number of reported occurrences. Our results extend previous work by considering sets of strings as well as by generalizing pairs to multirepeats.

Late developments in Web Service discovery approaches mainly focus on the incorporation of the QoS concept in the selection process. In this paper, we present a generalized design and implementation of a QoS enabled Web Service discovery mechanism. We describe a mechanism extending the propositions of [1] in a generalized environment that consists of dynamic groups/ categories of different Web Services. A UDDI search wrapper is introduced to facilitate the traditional WS discovery by taking into account QoS characteristics in transparent way. Our aim is the combination of the classic Web Service discovery, at design or execution time, with the benefit of QoS characteristics without affecting the existing UDDI search facilities. To evaluate the functionality, feasibility and effectiveness of the mechanism proposed, a number of experimental applications have been developed. All applications have been available for evaluation online in laboratory.

Understanding and modeling user online behavior, as well as predicting future requests remain an open challenge for researchers, analysts and marketers. In this paper, we propose an efficient prediction schema based on the extraction of sequential navigation patterns from server log files, combined with web site topology. Traversed paths are monitored, internally recorded and cleaned before being completed with cashed page views. After session and episode identification follows the construction of n-grams. Prediction is based upon a 5 + n-gram schema with all lower level n-grams participating, a procedure that resembles the construction of an All 5th-order Markov Model. The schema achieves full coverage while maintaining competitive prediction precision.

In our days the business, scientific and personal databases are growing in an exponential rate. However, what is truly valuable is the knowledge that can be extracted from the stored data. Knowledge Discovery in patent databases was traditionally based on manual analysis carried out from statistical experts. Nowadays the increasing interest of many actors have led to the development of new tools for discovering and exploiting information related to technological activities and innovation, "hidden" in patent databases. In this paper we present a system that combines efficient and innovative methodologies and tools for the analysis of patent data stored in international databases and the production of scientific and technological indicators.

Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. Each neurule is represented as an adaline unit. One way that the neurules can he produced is from training examples (empirical source knowledge). However, in certain application fields not all of the training examples are available a priori. A number of them become available over time. In these cases, updating the corresponding neurules is necessary. In this paper, methods for updating a hybrid rule base, consisting of neurules, to reflect the availability of new training examples are presented The methods are efficient, since they require the least possible retraining effort and the number of the produced neurules is kept as small as possible.

We develop a new finger search tree with worst-case constant update time in the Pointer Machine (PM) model of computation. This was a major problem in the field of Data Structures and was tantalizingly open for over twenty years while many attempts by researchers were made to solve it. The result comes as a consequence of the innovative mechanism that guides the rebalancing operations combined with incremental multiple splitting and fusion techniques over nodes.

Success on the global market has made the adoption of modern management and administration structures of prime importance. The administration of heterogeneous sets of services and applications necessitates the use of information technology, which facilitates cross-organisational process planning and tracking. We describe a virtual private network architecture that uses special monitoring and co-ordination processes in order to achieve efficient delivery of services and activities.

A new emerging area of research and technological development, set forward by the requirements of today's progressive industries, is the paradigm of virtual organisations. A meta-data repository is the logical place for uniformly retaining and managing corporate knowledge within or across different members of such organisations. Many meta-data architectures are not optimized for information retrieval purposes. Taking into consideration the existence of different data types and attributes, many problems arise when we try to successfully merge search results from heterogeneous sources. Motivated by these observations, this paper investigates a new architecture that can support multiple meta-data abstractions and that is readily extensible. Modularity and extensibility are the design emphases.

Describes a system offering a solution to the problem of ambulance management and emergency incident handling in the prefecture of Attica in Greece. It is based on a geographic information system (GIS) coupled with GPS (Global Positioning System) and GSM (Global System for Mobile Communication) technologies. The system's operation is expected to minimize the ambulances' response time. Consequently, there will be a drastic improvement in the way emergency incidents are being handled. This fact will thus significantly affect the quality of health services offered to citizens.

In this paper, we present the functional design and the technical specifications of a complete blood-transfusion information system, named the PELICAN system. This system is now under development in the University of Patras in co-operation with the Blood Centre of the University Hospital in Patras. We describe the main aspects of its architecture and the basic functionalities of the system.

We present simple algorithms for three problems belonging to the class of intersection query problems. The first algorithm deals with the static rectangle enclosure problem and can easily be extended to d dimensions, the second algorithm copes with the generalized c-oriented polygon intersection searching problem in two dimensions, while the third solves the static 2-dimensional dominance searching problem with respect to a set of obstacles. All algorithms are simple, are based on persistence and improve previous bounds. Also, as a corollary of the first algorithm, we present a result for the static d-dimensional range searching problem.

Let Q 1 be a set of'red' geometric objects and Q 2 a set of 'blue' ones. The objects in Q 1 and Q 2 are of arbitrarily large description size but each one is the union of simpler constant size components. We consider the problem of reporting all intersections between objects of Q 1 and objects of Q 2 in a time that depends on the size of the output. This is equivalent to painting the constant size objects in Q 1 and Q 2 and reporting colour intersections between Q 1 and Q 2 . We present a technique that yields simple output-sensitive algorithms for many kinds of geometric objects. We also show that, using the same technique, it is possible to report all intersecting pairs in a set of objects in an output-sensitive manner.

The retrieval capabilities of the signature file access method have become very attractive for data processing applications dealing with both formatted and unformatted data. Multimedia databases should also take advantage of this method, provided convenient storage structures for organizing signature files are available. In this paper a high performance dynamic signature file organization is proposed, combining the latest developments in storage structures (dynamic partitioning schemes) with the classical multilevel organizations in an efficient and effective way. We call the method the parametric weighted filter (PWF) and the performance analysis shows its superiority over previous methods. Finally, refinements are proposed that can further improve the scheme and that are worth further investigation.

In this paper we present a solution for a special case of the general rectangle intersection problem that has not been previously considered as a different case. This case, named the 'cross' intersection case, reports the set of these iso-oriented rectangles that intersect a query rectangle but do not enclose it and do not have one of their vertices inside it. We present solutions for unrestricted and restricted universe (grid) for the R d space. In the case of unrestricted d-dimensional space, the problem is solved in time O(log M ~ 3 nIoglogn + K) using O(/ilog M ~ 3 /i) space, where n is the number of rectangles and K is the size of the answer. In the case of restricted universe the same problem can be solved in O(\og d ~ 1 M+K) time and O{ity/\ogM ~ ) space, where M is the upper limit of the grid coordinates. Update operation in the dynamized version of the problem for the unrestricted and grid case is performed in 0(log 2< '~ 2 n) and O(\og d ~ i M) time, respectively.

We analyse the amortized behavior of AVL-trees under sequences of insertions. We show that the total rebalancing cost (=balance changes) for a sequence of n arbitrary insertions is at most $2.618n$. For random insertions the bound is improved to $2.26n$. We also show that the probability that t or more balance changes are required decreases exponentially with t.