DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association

microRNAs (miRNAs) are small endogenous RNA molecules that are implicated in many biological processes through post-transcriptional regulation of gene expression. The DIANA-microT Web server provides a user-friendly interface for comprehensive computational analysis of miRNA targets in human and mouse. The server has now been extended to support predictions for two widely studied species: Drosophila melanogaster and Caenorhabditis elegans. In the updated version, the Web server enables the association of miRNAs to diseases through bibliographic analysis and provides insights for the potential involvement of miRNAs in biological processes. The nomenclature used to describe mature miRNAs along different miRBase versions has been extensively analyzed, and the naming history of each miRNA has been extracted. This enables the identification of miRNA publications regardless of possible nomenclature changes. User interaction has been further refined allowing users to save results that they wish to analyze further. A connection to the UCSC genome browser is now provided, enabling users to easily preview predicted binding sites in comparison to a wide array of genomic tracks, such as single nucleotide polymorphisms. The Web server is publicly accessible in www.microrna.gr/microT-v4

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

On the usage of structural distance metrics for mining hierarhical structures

The recent proliferation of XML-based standards and technologies demonstrates the need for effective management of hierarchical structures. Such structures are used, for example, to organize data in product catalogs, taxonomies of thematic categories, concept hierarchies, etc. Since the XML language has become the standard data exchange format on the Web, organizing data in hierarchical structures has been vastly established. Even if data are not stored natively in such structures, export mechanisms make data publicly a vailable in hierarchical structures to enable its automatic processing by programs, scripts and agents

Search behavior-driven training for result re-ranking

In this paper we present a framework for improving the ranking learning process, taking into account the implicit search behaviors of users. Our approach is query-centric. That is, it examines the search behaviors induced by queries and groups together queries with similar such behaviors, forming search behavior clusters. Then, it trains multiple ranking functions, each one corresponding to one of these clusters. The trained models are finally combined to re-rank the results of each new query, taking into account the similarity of the query with each cluster. The main idea is that similar search behaviors can be detected and exploited for result re-ranking by analysing results into feature vectors, and clustering them. The experimental evaluation shows that our method improves the ranking quality of a state of the art ranking model

Sailing the web with captain Nemo: a personalized metasearch engine

Personalization on the Web is an issue that has gained a lot of interest lately. Web sites have already started providing services such as preferences for the interface, the layout and the functionality of the applications. Personalization services have also been introduced in Web search and metasearch engines, i.e. tools that retrieve Web pages relevant to keywords given by the users. However, those services deal mostly with the presentation style and ignore issues like the retrieval model, the ranking algorithm and topic pref- erences. In this paper, we present Captain Nemo, a fully-functionable metasearch engine that exploits personal user search spaces. Users can define their personal retrieval model and presentation style. They can also define topics of interest. Captain Nemo exploits several popular Web search engines to retrieve Web pages relevant to keywords given by the users. The resulting pages are presented according to the defined presentation style and retrieval model. For every page, Captain Nemo can recommend a relevant topic of interest to classify the page, exploiting nearest neighbour classification techniques

Modeling and manipulating the structure of hierarchical schemas for the web

The Semantic Web is the next step of the current Web where information will become more machine-understandable to support effective data discovery and integration. Hierarchical schemas, either in the form of tree-like structures (e.g., DTDs, XML schemas), or in the form of hierarchies on a category/subcategory basis (e.g., thematic hierarchies of portal catalogs), play an important role in this task. They are used to enrich semantically the available information. Up to now, hierarchical schemas have been treated rather as sets of individual elements, acting as semantic guides for browsing or querying data. Under that view, queries like "find the part of a portal catalog which is not present in another catalog" can be answered only in a procedural way, specifying which nodes to select and how to get them. For this reason, we argue that hierarchical schemas should be treated as full-fledged objects so as to allow for their manipulation. This work proposes models and operators to manipulate the structural information of hierarchies, considering them as first-class citizens. First, we explore the algebraic properties of trees representing hierarchies, and define a lattice algebraic structure on the