Identifying significant edges in graphical models of molecular networks
Marco Scutari; Radhakrishnan Nagarajan · 2013 · Artificial Intelligence in Medicine
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
OBJECTIVE: Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction with structure learning algorithms to determine significant associations. The present study overcomes this limitation by proposing a statistically motivated approach for identifying significant associations in a network. METHODS AND MATERIALS: A new method that identifies significa
Abstract by Marco Scutari; Radhakrishnan Nagarajan, Artificial Intelligence in Medicine (2013) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.artmed.2012.12.006