Resources for Life Sciences Data Management

BOOKS

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Next Generation Search Engines: Advanced Models for Information Retrieval Jouis, C, Biskri, I., Ganascia , J.G, Roux, M. (Eds.), IGI-Global, Hershey (Pa, USA), 2012.

With the rapid growth of web-based applications, such as search engines, the development of effective and personalized information retrieval techniques and user interfaces is essential. The amount of shared information has considerably grown, requiring metadata for new sources of information. These metadata have to provide classification information for a wide range of topics, each of which provides additional tagging information. Thus, it is an opportune time to identify ways to exploit such metadata in IR tasks such as user modeling, query understanding, and personalization, to name a few. Although the use of traditional metadata such as html text, web page titles, and anchor text is fairly well-understood, the use of category information, user behavior data, and geographical information, etc., is just beginning to be studied.

The main goal of this book is to transfer new research results from the fields of advanced computer sciences and information sciences, including e-sciences, to the design of new search engines.

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Biology at the digital age | Biologie: L'ère numérique (in French) Roux M. (Dir.), CNRS Editions, Paris (France), 2009.

The advent of digital technologies into Biology is changing the way knowledge is produced; this book is a reflection on these changes and authors, from private and public research leaders as well as corporate managers, provide important insights.
In Part I(chapter 1), Magali Roux (Research Director, CNRS) and colleagues, examines the nature of the changes at the four levels with regard to: (i) data production, (ii) data storage, (iii) data analyis and (iv) data dissemination; then, the question of whether or not digital methods constraint knowledge production, is discussed. Part II (chapters 2-6), presents scientific and industrial challenges related to these changes, notably regarding the pharmaceutical industry. In chapter 2, Denis Noble (professor Emeritus, University of Oxford) underlines the ten principles of Systems Biolgy in light of systems modeling and simulation. In chapter 6, Serge Bischoff (President, Rhenovia Pharma SAS) and Michel Faupel (COO, Rhenovia Pharma SAS) underline the huge cross-diciplinary efforts needed for developping customized approaches in Personalized Medicine. Part III (chapters 7-8) presents new tools and infrastuctures to support these changes. Well focused "Points of View" provided throughout the text by experts contribute to develop these new concepts for the purpose of assisting the reader.

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Systems Biology: Standards and Models | Biologie Systémique: Standards et Modèles (in French) Roux M. (Dir.), Omniscience, Paris (France), 2007.

Systems Biology is a cross-disciplinary approach of Life Sciences which broadens the field of Molecular Biology to study all the elements of the systems and their interactions, i. e. , to study molecular systems instead of single molecular entities. As a result, these approaches are producing a data deluge and data integration is becoming a paramount concern that requires a strong analytic base with regard to methods and implementation alike. To meet these needs, the international scientific community has taken various initiatives for the promotion and development of data standardization.

This pioneering book was written by 25 renowned experts in the field. It discusses in twelve chapters about standardization challenges: domain standards (standards for genomics, transcriptomics, etc.), languages (markup languages, ontologies, domain-specific languages, etc.), modeling formalisms (graphical formalisms, mathematical formalisms, etc. The "proof of concept" is illustrated through a cardiac cell model and its use in arrhythmia treatment. Last but not least, a "data integration" theory is proposed, based on Model-Driven Engineering. This approach allows eliciting integration architectures that do not oversimplify biological complexity but take it into account and retrieve it.