Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Antiepileptika bei Frauen im gebärfähigen Alter und in der Schwangerschaft : Vergleich der Fachinformationen in Deutschland und der Schweiz mit dem aktuellen Wissensstand [Antiepileptics in women of childbearing age and during pregnancy : Comparison of specialized information with the current state of knowledge in Germany and Switzerland].

BACKGROUND: Healthcare professionals regularly read the summary of product characteristics (SmPC) as one of the various sources of information on the risks of drug use in women of childbearing age and during pregnancy. The aim of this article is to present an overview of the teratogenic potential of various antiepileptic drugs and to compare these data with the information provided by the SmPCs. METHODS: A literature search on the teratogenic risks of 19 antiepileptic agents was conducted and the results were compared with the information on the use in women of childbearing age and during pregnancy provided by the SmPCs of 38 commercial products available in Switzerland and Germany. RESULTS: The teratogenic risk is discussed in all available SmPCs. Quantification of the risk for birth defects and the numbers of documented pregnancies are mostly missing. Reproductive safety information in SmPCs showed poor concordance with risk levels reported in the literature. Recommendations concerning the need to monitor plasma levels and possibly perform dose adjustments during pregnancy to prevent treatment failure were missing in five Swiss and two German SmPCs. DISCUSSION: The information regarding use in women of childbearing age and during pregnancy provided by the SmPCs is heterogeneous and poorly reflects the current state of knowledge. Regular updates of SmPCs are warranted in order for these documents to be of reliable use for health care professionals.

Extending Learning Objects by Means of Social Networking

Learning objects have been the promise of providing people with high quality learning resources. Initiatives such as MIT Open-CourseWare, MERLOT and others have shown the real possibilities of creating and sharing knowledge through Internet. Thousands of educational resources are available through learning object repositories. We indeed live in an age of content abundance, and content can be considered as infrastructure for building adaptive and personalized learning paths, promoting both formal and informal learning. Nevertheless, although most educational institutions are adopting a more open approach, publishing huge amounts of educational resources, the reality is that these resources are barely used in other educational contexts. This paradox can be partly explained by the dificulties in adapting such resources with respect to language, e-learning standards and specifications and, finally, granularity. Furthermore, if we want our learners to use and take advantage of learning object repositories, we need to provide them with additional services than just browsing and searching for resources. Social networks can be a first step towards creating an open social community of learning around a topic or a subject. In this paper we discuss and analyze the process of using a learning object repository and building a social network on the top of it, with respect to the information architecture needed to capture and store the interaction between learners and resources in form of learning object metadata.

Competitor Information andCompetitive Knowledge Management in a Large Industrial Organization

Thisthesis supplements the systematic approach to competitive intelligence and competitor analysis by introducing an information-processing perspective on management of the competitive environment and competitors therein. The cognitive questions connected to the intelligence process and also the means that organizational actors use in sharing information are discussed. The ultimate aim has been to deepen knowledge of the different intraorganizational processes that are used in acorporate organization to manage and exploit the vast amount of competitor information that is received from the environment. Competitor information and competitive knowledge management is examined as a process, where organizational actorsidentify and perceive the competitive environment by using cognitive simplification, make interpretations resulting in learning and finally utilize competitor information and competitive knowledge in their work processes. The sharing of competitive information and competitive knowledge is facilitated by intraorganizational networks that evolve as a means of developing a shared, organizational level knowledge structure and ensuring that the right information is in the right place at the right time. This thesis approaches competitor information and competitive knowledge management both theoretically and empirically. Based on the conceptual framework developed by theoretical elaboration, further understanding of the studied phenomena is sought by an empirical study. The empirical research was carried out in a multinationally operating forest industry company. This thesis makes some preliminary suggestions of improving the competitive intelligence process. It is concluded that managing competitor information and competitive knowledge is not simply a question of managing information flow or improving sophistication of competitor analysis, but the crucial question to be solved is rather, how to improve the cognitive capabilities connected to identifying and making interpretations of the competitive environment and how to increase learning. It is claimed that competitive intelligence can not be treated like an organizational function or assigned solely to a specialized intelligence unit.

Reconstruction of 3D Planar Objects with Partial Occlusion

Kolmiulotteisten kappaleiden rekonstruktio on yksi konenäön haastavimmista ongelmista, koska kappaleiden kolmiulotteisia etäisyyksiä ei voida selvittää yhdestä kaksiulotteisesta kuvasta. Ongelma voidaan ratkaista stereonäön avulla, jossa näkymän kolmiulotteinen rakenne päätellään usean kuvan perusteella. Tämä lähestymistapa mahdollistaa kuitenkin vain rekonstruktion niille kappaleiden osille, jotka näkyvät vähintään kahdessa kuvassa. Piilossa olevien osien rekonstruktio ei ole mahdollista pelkästään stereonäön avulla. Tässä työssä on kehitetty uusi menetelmä osittain piilossa olevien kolmiulotteisten tasomaisten kappaleiden rekonstruktioon. Menetelmän avulla voidaan selvittää hyvällä tarkkuudella tasomaisista pinnoista koostuvan kappaleen muoto ja paikka käyttäen kahta kuvaa kappaleesta. Menetelmä perustuu epipolaarigeometriaan, jonka avulla selvitetään molemmissa kuvissa näkyvät kappaleiden osat. Osittain piilossa olevien piirteiden rekonstruointi suoritetaan käyttämäen stereonäköä sekä tietoa kappaleen rakenteesta. Esitettyä ratkaisua voitaisiin käyttää esimerkiksi kolmiulotteisten kappaleiden visualisointiin, robotin navigointiin tai esineentunnistukseen.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

The role of business information and knowledge in sales steering

The objective of this study is to find out how sales management can be optimally supported with business information and knowledge. The first chapters of the study focus on theoretical literature about sales planning, sales steering, business intelligence, and knowledge management. The empirical part of the study is a case study for which the material was collected through interviews with the selected people of the company. The findings from the interviews were analyzed, and possible suggestions for solving the problems were made. The case study revealed that sales management requires a multitude of metrics and reports to steer the sales to the desired direction. The information sources can be internal and external, and the optimal solution for satisfying the information needs is a combination of both of these. The simple information should be turned into knowledge by merging the intellectual assets with the information from the firm’s transaction processing systems, in order to promote organizational learning and effective decision-making.

Lectio praecursoria : Systems between information and knowledge : in a memory management model of an extended enterprise

VTT Jouni Meriluodon valtio-opin alaan kuuluva väitöskirja Systems between information and knowledge : in a memory management model of an extended enterprise tarkastettiin 21.6.2011 Helsingin yliopistossa.