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The speed and accuracy of new scientific discoveries - be it by humans or artificial intelligence - depends on the quality of the underlying data and on the technology to connect, search and share the data efficiently. In recent years, we have seen the rise of graph databases and semi-formal data models such as knowledge graphs to facilitate software approaches to scientific discovery. These approaches extend work based on formalised models, such as the Semantic Web. In this paper, we present our developments to connect, search and share data about genome-scale knowledge networks (GSKN). We have developed a simple application ontology based on OWL/RDF with mappings to standard schemas. We are employing the ontology to power data access services like resolvable URIs, SPARQL endpoints, JSON-LD web APIs and Neo4j-based knowledge graphs. We demonstrate how the proposed ontology and graph databases considerably improve search and access to interoperable and reusable biological knowledge (i.e. the FAIRness data principles).
This article was published in the following journal.
Name: Journal of integrative bioinformatics
The important topic of multistability of continuous- and discrete-time neural network (NN) models has been investigated rather extensively. Concerning the design of associative memories, multistabilit...
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Screening techniques first developed in yeast to identify genes encoding interacting proteins. Variations are used to evaluate interplay between proteins and other molecules. Two-hybrid techniques refer to analysis for protein-protein interactions, one-hybrid for DNA-protein interactions, three-hybrid interactions for RNA-protein interactions or ligand-based interactions. Reverse n-hybrid techniques refer to analysis for mutations or other small molecules that dissociate known interactions.
Devices for simulating the activities of the liver. They often consist of a hybrid between both biological and artificial materials.
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Proteins produced from GENES that have mutated by the fusing of protein coding regions of more than one gene. Such hybrid proteins are responsible for some instances of ANTIBIOTIC RESISTANCE and defective biological processes such as NEOPLASMS.
Complex pharmaceutical substances, preparations, or agents of organic origin, usually obtained by biological methods or assay. They are used especially in diagnosis and treatment of disease (as vaccines or pollen extracts). Biological products are differentiated from BIOLOGICAL FACTORS in that the latter are compounds with biological or physiological activity made by living organisms. (From Webster's 3d ed)
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Bioinformatics is the application of computer software and hardware to the management of biological data to create useful information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied...