Giorgos Giannopoulos

DIANA-microT web server: elucidating microRNA functions through target prediction

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

Accurate microRNA target prediction correlates with protein repression levels.

BackgroundMicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease.ResultsDIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction.ConclusionRecently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

Integrating keywords and semantics on document annotation and search

This paper describes GoNTogle, a framework for document annotation and retrieval, built on top of Semantic Web and IR technologies. GoNTogle supports ontology-based annotation for documents of several formats, in a fully collaborative environment. It provides both manual and automatic annotation mechanisms. Automatic annotation is based on a learning method that exploits user annotation history and textual information to automatically suggest annotations for new documents. GoNTogle also provides search facilities beyond the traditional keyword-based search. A flexible combination of keyword-based and semantic-based search over documents is proposed in conjunction with advanced ontology-based search operations. The proposed methods are implemented in a fully functional tool and their effectiveness is experimentally validated.

GoNTogle: a tool for semantic annotation and search

This paper presents GoNTogle, a tool which provides advanced document annotation and search facilities. GoNTogle allows users to annotate several document formats, using ontology concepts. It also produces automatic annotation suggestions based on textual similarity and previous document annotations. Finally, GoNTogle combines keyword and semantic-based search, offering advanced ontology query facilities.

Diversifying user comments on news articles

In this paper we present an approach for diversifying user comments on news articles. In our proposed framework, we analyse user comments w.r.t. four different criteria in order to extract the respective diversification dimensions in the form of feature vectors. These criteria involve content similarity, sentiment expressed within comments, articles named entities also found within comments and commenting behavior of the respective users. Then, we apply diversification on comments, utilizing the extracted features vectors. The outcome of this process is a subset of the initial comments that contains heterogeneous comments, representing different aspects of the news article, different sentiments expressed, as well as different user categories, w.r.t. their commenting behavior. We perform a preliminary qualitative analysis showing that the diversity criteria we introduce result in distinctively diverse subsets of comments, as opposed to a baseline of diversifying comments only w.r.t. to their content (textual similarity). We also present a prototype system that implements our diversification framework on news articles comments.

Towards GeoSpatial semantic data management: strengths, weaknesses, and challenges ahead

An immense wealth of data is already accessible through the Semantic Web and an increasing part of it also has geospatial context or relevance. Although existing technology is mature enough to integrate a variety of information from heterogeneous sources into interlinked features, it still falls behind when it comes to representation and reasoning on spatial characteristics. It is only lately that several RDF stores have begun to accommodate geospatial entities and to enable some kind of processing on them. To address interoperability, the OGC has recently adopted the GeoSPARQL standard, which defines a vocabulary for representing geometric types in RDF and an extension to the SPARQL language for formulating queries. In this paper, we provide a comprehensive review of the current state-of-the-art in geospatially-enabled semantic data management. Apart from an insightful analysis of the available architectures in industry and academia, we conduct an evaluation study on prominent RDF stores with geospatial support. We also compare their performance and attested capabilities to renowned DBMSs widely used in geospatial applications. We introduce a methodology suitable to assess RDF stores for robustness against large geospatial datasets, and also for expressiveness on a variety of queries involving both spatial and thematic criteria. As our findings demonstrate, the potential for query optimization, advanced indexing schemes, and spatio-semantic extensions is significant. Towards this goal, we point out several challenging issues for joint research by the GIS and Semantic Web communities.

Learning to rank user intent

Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus rendering the deployment of personalized models challenging. We take a different approach and study ranking models for user intent. We exploit user feedback in terms of click data to cluster ranking models for historic queries according to user behavior and intent. Each cluster is finally represented by a single ranking model that captures the contained search interests expressed by users. Once new queries are issued, these are mapped to the clustering and the retrieval process diversifies possible intents by combining relevant ranking functions. Empirical evidence shows that our approach significantly outperforms baseline approaches on a large corporate query log.

Algorithms and criteria for diversification of news article comments

In this paper, we introduce an approach for diversifying user comments on news articles. We claim that, although content diversity suffices for the keyword search setting, as proven by existing work on search result diversification, it is not enough when it comes to diversifying comments of news articles. Thus, in our proposed framework, we define comment-specific diversification criteria in order to extract the respective diversification dimensions in the form of feature vectors. These criteria involve content similarity, sentiment expressed within comments, named entities, quality of comments and combinations of them. Then, we apply diversification on comments, utilizing the extracted features vectors. The outcome of this process is a subset of the initial set that contains heterogeneous comments, representing different aspects of the news article, different sentiments expressed, different writing quality, etc. We perform an experimental analysis showing that the diversity criteria we introduce result in distinctively diverse subsets of comments, as opposed to the baseline of diversifying comments only w.r.t. to their content. We also present a prototype system that implements our diversification framework on news articles comments.

OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap

In this demonstration, we present OSMRec, a command line utility and JOSM plugin for automatic recommendation of tags (categories) on newly created spatial entities in OpenStreetMap (OSM). JOSM allows downloading parts of OSM, editing the map (e.g. inserting, deleting, annotating with tags spatial entities) and re-uploading the updated part back on OSM. OSMRec plugin exploits already annotated entities within OSM to train category classification models and utilizes these models in order to recommend OSM categories for newly inserted spatial entities in OSM.

Diversifying microblog posts

Microblogs have become an important source of information, a medium for following and spreading trends, news and ideas all over the world. As a result, microblog search has emerged as a new option for covering user information needs, especially with respect to timely events, news or trends. However users are frequently overloaded by the high rate of produced microblogging posts, which often carry no new information with respect to other similar posts. In this paper we propose a method that helps users effectively harvest information from a microblogging stream, by filtering out redundant data and maximizing diversity among the displayed information. We introduce microblog posts-specific diversification criteria and apply them on heuristic diversification algorithms. We implement the above methods into a prototype system that works with data from Twitter. The experimental evaluation, demonstrates the effectiveness of applying our problem specific diversification criteria, as opposed to applying plain content diversity on microblog posts.