The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis

Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research

Leveraging Peer-to-Peer and Ontologies for the Extended Enterprise

In recent years, organisations are blurring their boundaries interacting with other organisations. This process fostered new business paradigms and organisational forms that transcend the previous static and closed competitive models and move to flexible and collaborative ways of working. Examples of new models are the extended enterprise (EE) and virtual enterprise (VE). For promoting those new organisational models are required adequate technologies enabling collaboration, integration and exchanging of information across heterogeneous and distributed sources. Moreover, in such environments, another important aspect to deal with is related to the |quality| of information and knowledge exchanged. For fulfilling those requirements, we argue that a peer-to-peer (P2P) architecture implementing the |virtual office| paradigm combined with adequate semantic supports can be an effective solution. This paper presents K-link+, a P2P system implemented in JXTA based on the concepts of |community of business|, |virtual office| and |ontologies|

Refining Node Embeddings via Semantic Proximity

There is a variety of available approaches to learn graph node embeddings. One of their common underlying task is the gener- ation of (biased) random walks that are then fed into representation learning techniques. Some techniques generate biased random walks by using structural information. Other approaches, also rely on some form of semantic information. While the former are purely structural, thus not fully considering knowledge available in semantically rich networks, the latter require complex inputs (e.g., metapaths) or only leverage node types that may not be available. The goal of this paper is to overcome these limitations by introducing NESP(Node Embeddings via Semantic Proximity), which features two main components. The first provides four different ways of biasing random walks by leveraging semantic relatedness between predicates. The second component focuses on refining (existing) embeddings by leveraging the notion of semantic proximity. This component iteratively refines an initial set of node embeddings imposing the embeddings of semantic neighboring nodes of a node to lie within a sphere of fixed radius. We discuss an extensive experimental evaluation and comparison with related work

A semantic similarity framework exploiting multiple parts-of-speech

pirro2010aInternational audienceSemantic similarity aims at establishing resemblance by interpreting the meaning of the objects being compared. The Semantic Web can benefit from semantic similarity in several ways: ontology alignment and merging, automatic ontology construction, semantic-search, to cite a few. Current approaches mostly focus on computing similarity between nouns. The aim of this paper is to define a framework to compute semantic similarity even for other grammar categories such as verbs, adverbs and adjectives. The framework has been implemented on top of WordNet. Extensive experiments confirmed the suitability of this approach in the task of solving English tests