25 research outputs found
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DiseaseConnect: a comprehensive web server for mechanism-based diseaseâdisease connections
The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover diseaseâdisease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drugâdisease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments
The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space
Graph data management is instrumental for several use cases such as
recommendation, root cause analysis, financial fraud detection, and enterprise
knowledge representation. Efficiently supporting these use cases yields a
number of unique requirements, including the need for a concise query language
and graph-aware query optimization techniques. The goal of the Linked Data
Benchmark Council (LDBC) is to design a set of standard benchmarks that capture
representative categories of graph data management problems, making the
performance of systems comparable and facilitating competition among vendors.
LDBC also conducts research on graph schemas and graph query languages. This
paper introduces the LDBC organization and its work over the last decade
Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation
The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks
The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space
Graph data management is instrumental for several use cases
such as recommendation, root cause analysis, financial fraud detection,
and enterprise knowledge representation. Efficiently supporting these use
cases yields a number of unique requirements, including the need for a
concise query language and graph-aware query optimization techniques.
The goal of the Linked Data Benchmark Council (LDBC) is to design
a set of standard benchmarks that capture representative categories of
graph data management problems, making the performance of systems
comparable and facilitating competition among vendors. LDBC also
conducts research on graph schemas and graph query languages. This
paper introduces the LDBC organization and its work over the last decade
Elastic and anelastic relaxation behaviour of perovskite multiferroics II: PbZr0.53Ti0.47O3 (PZT)âPbFe0.5Ta0.5O3 (PFT)
A novel context-aware recommendation algorithm with two-level SVD in social networks
With the rapid development of Internet applications and social networks, we have entered an era of big data, and people are hard to effectively find the information they want. Therefore, lots of recommendation algorithms have been proposed to help users select useful and beneficial information, and save their time. Moreover, context-aware recommendation methods are becoming more and more popular since they could provide more accurate or personalized recommendation information, compared with traditional recommendation methods. Singular value decomposition (SVD) has been successfully integrated with some traditional recommendation algorithms. However, the basic SVD can only extra
Shaking table test and numerical analysis of a 1:12 scale model of a special concentrically braced steel frame with pinned connections
A Planar, Pressure-Balanced, Reconnecting Structure Embedded in a Small Solar Wind Transient. AIP Conference Proceedings
We describe a âŒ4 hour-long solar wind transient observed by the Wind spacecraft, in which is embedded a pressure-balanced structure. Minimum variance analysis on high resolution (âŒ11 Hz) magnetic field data shows it to be planar to an excellent approximation (ratio of intermediate-to-minimum eigenvalues = 83). The structure starts with a very sharp discontinuity whose orientation coincides within four degrees with that of the structure itself. We find that this discontinuity has a bifurcated magnetic field and plasma flow structure. There is also a velocity depression coextensive with it. Applying a tangential stress balance test (WalĂ©n relation) to the discontinuity, we find good agreement of predictions with observations. We show directly the presence of two AlfvĂ©n waves propagating in opposite directions. The observation is consistent with the presence of a reconnection region in a hitherto unexplored configuration within a small solar wind transient