PyPedia:using the wiki paradigm as crowd sourcing environment for bioinformatics protocols

Background: Today researchers can choose from many bioinformatics protocols for all types of life sciences research, computational environments and coding languages. Although the majority of these are open source, few of them possess all virtues to maximize reuse and promote reproducible science. Wikipedia has proven a great tool to disseminate information and enhance collaboration between users with varying expertise and background to author qualitative content via crowdsourcing. However, it remains an open question whether the wiki paradigm can be applied to bioinformatics protocols. Results: We piloted PyPedia, a wiki where each article is both implementation and documentation of a bioinformatics computational protocol in the python language. Hyperlinks within the wiki can be used to compose complex workflows and induce reuse. A RESTful API enables code execution outside the wiki. Initial content of PyPedia contains articles for population statistics, bioinformatics format conversions and genotype imputation. Use of the easy to learn wiki syntax effectively lowers the barriers to bring expert programmers and less computer savvy researchers on the same page. Conclusions: PyPedia demonstrates how wiki can provide a collaborative development, sharing and even execution environment for biologists and bioinformaticians that complement existing resources, useful for local and multi-center research teams. Availability: PyPedia is available online at: http://www.pypedia.com. The source code and installation instructions are available at: https://github.com/kantale/PyPedia_server. The PyPedia python library is available at: https://github.com/kantale/pypedia. PyPedia is open-source, available under the BSD 2-Clause License

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others

European efforts in nanoinformatics research applied to nanomedicine

Nanomedicine and Nanoinformatics are emerging disciplines with substantial challenges ahead. For instance, Nanomedicine involves complex and massive data analysis. Nanoinformatics could expand previous experiences in Biomedical Informatics with new features required to study different scientific biological and physical characteristics at a different level of complexity. ACTION-Grid is a project, funded by the European Commission, which aims to the creation of a collaborative environment in Biomedical and Nanomedical research among countries in Europe, Western Balkans, Latin America and North Africa. In this paper, we briefly review the concepts of Nanomedicine and Nanoinformatics and then we describe the activities of some of the ACTION-Grid consortium members considering those initiatives related to Nanomedicine

Interactive Knowledge Base Construction and Maintenance

This paper presents a methodology that supports medical knowledge acquisition and maintenance. The methodology integrates expert with empirical knowledge

Induction of "In-between " Classes: Learning Vague Concepts

ABSTRACT: This paper presents a synergistic iterative process, SIR, for resolving between classes assigned to cases. The "vagueness " of concepts, represented by multi-class assignment to cases, is a common phenomenon in the context of concept learning from examples (CLFE) paradigm. The causes could be attributed to the specifics of the application domain, to the poor initial representation or, to the learning heuristics themselves. The methodology presented in this paper take advantage of multi-class assignment in order to improve learning results. Our methodology implements a two-step iterative process: (1) an inductive algorithm runs on the training set of cases, and (2) application of a specially devised set of heuristics aiming to invent new classes, and resolve the conflict presented by multi-class assignment. Thus, "vague " concepts, laying "in-between " the underlying concepts, are learned. Experiments on real-world domains from medicine and finance are presented, and the utility of the SIR process in decision-making tasks is discussed