diceCoefficient

<p>Data frame with column names: Experiment.id, Replicate, Bait,
Prey, and count (i.e., prey count).</p>

datInput

<p>This function applies Dice coefficient to score instances
(e.g., bait-prey interactions (BPIs) in the data.frame.The Dice coefficient was first applied by Zhang et al., 2008
to score interactions between all identified proteins (baits and preys) in a given AP-MS experiment.</p>

The 'MSiP' is a computational approach to predict protein-protein interactions from large-scale affinity purification mass 'spectrometry' (AP-MS) data. This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions.

diceCoefficient: diceCoefficient

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