Cooperative E-Organizations for Distributed Bioinformatics Experiments

Large-scale collaboration is a key success factor in today scientific experiments, usually involving a variety of digital resources, while Cooperative Information Systems (CISs) represent a feasible solution for sharing distributed information sources and activities. On this premise, the aim of this paper is to provide a paradigm for modeling scientific experiments as distributed processes that a group of scientists may go through on a network of cooperative e-nodes interacting with one another in order to offer or to ask for services. By discussing a bioinformatics case study, the paper details how the problem solving strategy related to a scientific experiment can be expressed by a workflow of single cooperating activities whose implementation is carried out on a prototypical service-based scientific environment.

[1]  Nicoletta Dessì,et al.  Capturing Heuristics and Intelligent Methods for Improving Micro-array Data Classification , 2007, IDEAL.

[2]  William B. Rouse Wiley Series in Systems Engineering and Management , 2005 .

[3]  Bob Travica,et al.  Virtual organization and electronic commerce , 2005, DATB.

[4]  Maria Grazia Fugini,et al.  Applying Enterprise Models to Design Cooperative Scientific Environments , 2005, Business Process Management Workshops.

[5]  Munindar P. Singh,et al.  Service-Oriented Computing: Semantics, Processes, Agents , 2010 .

[6]  Guido Governatori,et al.  Compliance aware business process design , 2008 .

[7]  John Fulcher,et al.  Advances in Applied Artificial Intelligence , 2006 .

[8]  Yang Li,et al.  Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning , 2007, IDEAL.

[9]  Ralph Hodgson,et al.  Adaptive information - improving business through semantic interoperability, grid computing, and enterprise integration , 2004, Wiley series in systems engineering and management.

[10]  Francine Berman,et al.  Grid Computing: Making the Global Infrastructure a Reality , 2003 .

[11]  J. Downing,et al.  Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.

[12]  James A. Hendler,et al.  Guest Editors' Introduction: E-Science , 2004, IEEE Intelligent Systems.

[13]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[14]  Gustavo Alonso,et al.  Web Services: Concepts, Architectures and Applications , 2009 .

[15]  Nicoletta Dessì,et al.  High-Dimensional Micro-array Data Classification Using Minimum Description Length and Domain Expert Knowledge , 2006, IEA/AIE.

[16]  Nicoletta Dessì,et al.  Learning Bayesian Classifiers from Gene-Expression MicroArray Data , 2005, WILF.