p-Medicine: From data sharing and integration via VPH models to personalized medicine

The Worldwide innovative Networking in personalized cancer medicine (WIN) initiated by the Institute Gustave Roussy (France) and The University of Texas MD Anderson Cancer Center (USA) has dedicated its 3rd symposium (Paris, 6–8 July 2011) to discussion on gateways to increase the efficacy of cancer diagnostics and therapeutics (http://www.winconsortium.org/symposium.html)

Identification of a Putative Crf Splice Variant and Generation of Recombinant Antibodies for the Specific Detection of Aspergillus fumigatus

BACKGROUND: Aspergillus fumigatus is a common airborne fungal pathogen for humans. It frequently causes an invasive aspergillosis (IA) in immunocompromised patients with poor prognosis. Potent antifungal drugs are very expensive and cause serious adverse effects. Their correct application requires an early and specific diagnosis of IA, which is still not properly achievable. This work aims to a specific detection of A. fumigatus by immunofluorescence and the generation of recombinant antibodies for the detection of A. fumigatus by ELISA. RESULTS: The A. fumigatus antigen Crf2 was isolated from a human patient with proven IA. It is a novel variant of a group of surface proteins (Crf1, Asp f9, Asp f16) which belong to the glycosylhydrolase family. Single chain fragment variables (scFvs) were obtained by phage display from a human naive antibody gene library and an immune antibody gene library generated from a macaque immunized with recombinant Crf2. Two different selection strategies were performed and shown to influence the selection of scFvs recognizing the Crf2 antigen in its native conformation. Using these antibodies, Crf2 was localized in growing hyphae of A. fumigatus but not in spores. In addition, the antibodies allowed differentiation between A. fumigatus and related Aspergillus species or Candida albicans by immunofluorescence microscopy. The scFv antibody clones were further characterized for their affinity, the nature of their epitope, their serum stability and their detection limit of Crf2 in human serum. CONCLUSION: Crf2 and the corresponding recombinant antibodies offer a novel approach for the early diagnostics of IA caused by A. fumigatus

Big Data in Medizin und Gesundheitswesen

Healthcare is one of the business fields with the highest Big Data potential. According to the prevailing definition, Big Data refers to the fact that data today is often too large and heterogeneous and changes too quickly to be stored, processed, and transformed into value by previous technologies. The technological trends drive Big Data: business processes are more and more executed electronically, consumers produce more and more data themselves e.g. in social networks and finally ever increasing digitalization. Currently, several new trends towards new data sources and innovative data analysis appear in medicine and healthcare. From the research perspective, omics-research is one clear Big Data topic. In practice, the electronic health records, free open data and the quantified self offer new perspectives for data analytics. Regarding analytics, significant advances have been made in the information extraction from text data, which unlocks a lot of data from clinic al documentation for analytics purposes. At the same time, medicine and healthcare is lagging behind in the adoption of Big Data approaches. This can be traced to particular problems regarding data complexity and organizational, legal, and ethical challenges. The growing uptake of Big Data in general and first best-practice examples in medicine and healthcare in particular, indicate that innovative solutions will be coming. This paper gives an overview of the potentials of Big Data in medicine and healthcare

On subgroup discovery in numerical domains

Subgroup discovery is a Knowledge Discovery task that aims at finding subgroups of a population with high generality and distributional unusualness. While several subgroup discovery algorithms have been presented in the past, they focus on databases with nominal attributes or make use of discretization to get rid of the numerical attributes. In this paper, we illustrate why the replacement of numerical attributes by nominal attributes can result in suboptimal results. Thereafter, we present a new subgroup discovery algorithm that prunes large parts of the search space by exploiting bounds between related numerical subgroup descriptions. The same algorithm can also be applied to ordinal attributes. In an experimental section, we show that the use of our new pruning scheme results in a huge performance gain when more that just a few split-points are considered for the numerical attributes

Workflow analysis using graph kernels

Workflow enacting systems are a popular technology in business and e-science alike to flexibly define and enact complex data processing tasks. Since the construction of a workflow for a specific task can become quite complex, efforts are currently underway to increase the re-use of workflows through the implementation of specialized workflow repositories. While existing methods to exploit the knowledge in these repositories usually consider workflows as an atomic entity, our work is based on the fact that workflows can naturally be viewed as graphs. Hence, in this paper we investigate the use of graph kernels for the problems of workflow discovery, workflow recommendation, and workflow pattern extraction, paying special attention on the typical situation of few labeled and many unlabeled workflows. To empirically demonstrate the feasibility of our approach we investigate a dataset of bioinformatics workflows retrieved from the website myexperiment.org