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This paper introduces a new methodology for discovering patterns in foodborne disease outbreaks using a data-driven approach.
High-throughput screening (HTS) of small-molecule compound collections is a well-validated and efficient methodology for discovering new lead molecules for drug discovery.
Data mining (i.e. knowledge discovery) provides a structured methodology for discovering and quantifying patterns hidden in large quantities of historical and operational data.
This paper strives to overcome a major problem encountered by a previous expansion methodology for discovering concepts highly likely to be missing a specific semantic type assignment in the UMLS.
In this work, we have presented a new methodology for discovering dynamical mechanisms induced by a small perturbation in a metabolic network, where kinetic information is lacking.
Our methodology for discovering ontology nonalignments follows from this global consistency enforcement.
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This makes drug formulation a good test case for methodologies for discovering and optimizing complex chemical systems.
Methodologies for discovering recombination hotspots and descriptions of their properties have been reviewed from the perspectives of mammals [ 3, 4] and plants [ 5].
Ultimately, as phenotypic screens have been successful tools for discovering potent antibacterial drugs, the HT-SPOTi methodology has the potential to identify new antimicrobial molecules with clinical relevance.
The core algorithm for discovering SSR patterns from genome sequences employs autocorrelation methodology.
Details of each procedure of these two phases are described in the following sections: The core algorithm for discovering SSR patterns from genome sequences employs autocorrelation methodology.
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CEO of Professional Science Editing for Scientists @ prosciediting.com