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.
This paper introduces a new methodology for discovering patterns in foodborne disease outbreaks using a data-driven approach.
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.
High-throughput screening (HTS) of small-molecule compound collections is a well-validated and efficient methodology for discovering new lead molecules for drug discovery.
As the nucleotide sequence of protein coding genes is ∼1% of the whole genome, analysis of exomes is considered an advantageous and cost effective methodology for discovering the genes involved in neoplastic development.
Our methodology for discovering ontology nonalignments follows from this global consistency enforcement.
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].
Here, a methodology for mapping specific biological pathways across several model species and discovering conserved TF-binding motifs from related homologous genes is established for conserved pathway analysis.
