Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

<p>Abstract</p> <p>Background</p> <p>Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of <it>k</it>-partite graphs. These graphs contain <it>k </it>different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type.</p> <p>Results</p> <p>Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a <it>k</it>-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted <it>k</it>-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2.</p> <p>Conclusions</p> <p>In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy <it>k</it>-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.</p

Tolerability of inhaled N-chlorotaurine in the pig model

<p>Abstract</p> <p>Background</p> <p>N-chlorotaurine, a long-lived oxidant produced by human leukocytes, can be applied in human medicine as an endogenous antiseptic. Its antimicrobial activity can be enhanced by ammonium chloride. This study was designed to evaluate the tolerability of inhaled N-chlorotaurine (NCT) in the pig model.</p> <p>Methods</p> <p>Anesthetized pigs inhaled test solutions of 1% (55 mM) NCT (n = 7), 5% NCT (n = 6), or 1% NCT plus 1% ammonium chloride (NH₄Cl) (n = 6), and 0.9% saline solution as a control (n = 7), respectively. Applications with 5 ml each were performed hourly within four hours. Lung function, haemodynamics, and pharmacokinetics were monitored. Bronchial lavage samples for captive bubble surfactometry and lung samples for histology and electron microscopy were removed.</p> <p>Results</p> <p>Arterial pressure of oxygen (PaO₂) decreased significantly over the observation period of 4 hours in all animals. Compared to saline, 1% NCT + 1% NH₄Cl led to significantly lower PaO₂values at the endpoint after 4 hours (62 ± 9.6 mmHg vs. 76 ± 9.2 mmHg, p = 0.014) with a corresponding increase in alveolo-arterial difference of oxygen partial pressure (AaDO₂) (p = 0.004). Interestingly, AaDO₂was lowest with 1% NCT, even lower than with saline (p = 0.016). The increase of pulmonary artery pressure (PAP) over the observation period was smallest with 1% NCT without difference to controls (p = 0.91), and higher with 5% NCT (p = 0.02), and NCT + NH₄Cl (p = 0.05).</p> <p>Histological and ultrastructural investigations revealed no differences between the test and control groups. The surfactant function remained intact. There was no systemic resorption of NCT detectable, and its local inactivation took place within 30 min. The concentration of NCT tolerated by A549 lung epithelial cells <it>in vitro </it>was similar to that known from other body cells (0.25–0.5 mM).</p> <p>Conclusion</p> <p>The endogenous antiseptic NCT was well tolerated at a concentration of 1% upon inhalation in the pig model. Addition of ammonium chloride in high concentration provokes a statistically significant impact on blood oxygenation.</p

Large scale knowledge extraction from biomedical literature based on semantic role labeling.

This doctorate aimed at the development of a broad scale text mining approach covering a multitude of relation types (e.g. activation, inhibition, phosphorylation and others) as well as entity types (e.g. genes, metabolites, diseases and others). The resulting text mining system EXCERBT was developed, optimized and evaluated in hindsight on practical usability for systems biology. The system is characterized in technical hindsight by high processing speed and easy extensibility. EXCERBT is a semantic search engine for biomedical texts additionally comprising a new approach for automatically generating biomedical lexica

Large scale knowlege representation of distributed biomedical information scaling topic maps.

Within the last years the Web dramatically influenced biomedical research. Although it allows for almost instantaneous access to a huge amount of distributed information the problem how to retrieve useful information still persist. With semantic technologies (especially Topic Maps) the solution becomes tangible. We will discuss in this paper concepts and a technical realization for knowledge representation within the biomedical domain. This includes not only the semantic access of distributed and heterogeneous resources based on state-of-the-art enterprise integration technologies (J2EE, Web Services) but also an approach for Topic Map based views on unstructured information from scientific publications. We will furthermore present the implementation of an information portal based on the seamless semantic integration of ~ 500 genome databases and ~16.000.000 abstracts

Photochemical incorporation of silver quantum dots in monodisperse silica colloids for photonic crystal applications

We developed a novel method to fabricate nanocomposite monodisperse SiO2 spheres (∼ 100 nm) containing homogeneously dispersed Ag quantum dots (2∼5 nm). The inclusion morphology is controlled through the timing of the photochemical reduction of silver ions during hydrolysis of tetraethoxysilane in a microemulsion. Depending on the timing, Ag quantum dots can be directed to different annuli within the SiO2 spheres, as well as onto the SiO2 sphere surfaces. The embedded Ag quantum dots show a plasmon resonance absorption band at 438 nm. These Ag@SiO2 particles have significant surface charge and readily self-assemble into crystalline colloidal array (CCA) photonic crystals which Bragg-diffract light in the visible region. The magnitude of the plasmon resonance absorption depends on the CCA Bragg diffraction condition. The negative dielectric constant of the silver nanoparticles may be decreasing the silica-silver nanodot composite refractive index below that of the water medium. We may be observing an analogue of the Borrmann effect previously observed in X-ray scattering, where the incident and diffracted electric field standing wave becomes localized in regions of small CCA crystal absorption