EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data

Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

Collaborative Filtering with Semantic Neighbour Discovery

Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items – the subset of items co-rated by both users – typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process – a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.

Cultivating Collaborative Relationships: A Case Study of Teacher Collaboration and Professional Development

Students often receive instruction from specialists, professionals other than their general educators, such as special educators, reading specialists, and ESOL (English Speakers of Other Languages) teachers. The purpose of this study was to examine how general educators and specialists develop collaborative relationships over time within the context of receiving professional development. While collaboration is considered essential to increasing student achievement, improving teachers’ practice, and creating comprehensive school reform, collaborative partnerships take time to develop and require multiple sources of support. Additionally, both practitioners and researchers often conflate collaboration with structural reforms such as co-teaching. This study used a retrospective single case study with a grounded theory approach to analysis. Data were collected through semi-structured interviews with thirteen teachers and an administrator after three workshops were conducted throughout the school year. The theory, Cultivating Interprofessional Collaboration, describes how interprofessional relationships grow as teachers engage in a cycle of learning, constructing partnership, and reflecting. As relationships deepen some partners experience a seamless dimension to their work. A variety of intrapersonal, interpersonal, and external factors work in concert to promote this growth, which is strengthened through professional development. In this theory, professional development provides a common ground for strengthening relationships, knowledge about the collaborative process, and a reflective space to create new collaborative practices. Effective collaborative practice can lead to aligned instruction and teachers’ own professional growth. This study has implications for school interventions, professional development, and future research on collaboration in schools.

Applying Platform Design to Improve Product-Service Systems Collaborative Development

Part 3: Product-Service Systems

Bioqueries: a collaborative environment to create, explore and share SPARQL queries in Life Sciences

Bioqueries provides a collaborative environment to create, explore, execute, clone and share SPARQL queries (including Federated Queries). Federated SPARQL queries can retrieve information from more than one data source.

Collaborative Systems for Smart Networked Environments

International audience

Integration of Social Concerns in Collaborative Logistics and Transportation Networks

Part 21: Mobility and Logistics

A Collaborative IT Network for Three Adriatic Maritime Ports

Part 21: Mobility and Logistics

Collaborative Management of Intermodal Mobility

Part 21: Mobility and Logistics

An AAL Collaborative System: The AAL4ALL and a Mobile Assistant Case Study

Part 20: Health and Care Networks

Bioqueries: a collaborative environment to create, explore and share SPARQL queries in Life Sciences

Bioqueries provides a collaborative environment to create, explore, execute, clone and share SPARQL queries (including Federated Queries). Federated SPARQL queries can retrieve information from more than one data source.

Forms of Collaboration in Collaborative Enterprises: An Ontological Model

Part 19: Knowledge Management in Networks

Combining Collaborative Networks and Knowledge Management: The SENAI Case

Part 19: Knowledge Management in Networks

L’alerte éthique : vers une gouvernance omnicanal ?

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