floating2: Filter targets

In timma: Target Inhibition Interaction using Maximization and Minimization Averaging

Description

A function to filter targets based on their corration with the drug sensitivity

Usage

floating2(profile, sens, sp = 1, max_k = 2, verbosity = FALSE)

Arguments

profile	drug-target interaction data
sens	drug sensitivity data
sp	an integer to specify the starting point for the sffs search algorithm. The number cannot be larger than the total number of targets in the drug-target interaction data. By default, the starting point is the first target, namely, sp = 1.
max_k	an integer to specify the maximal number of targets that can be selected by the sffs algorithm. In practice it is advised to keep it under 10 as the number of sensitivities to be predicted will increase exponentially. By default, max_k = 2.
verbosity	a boolean value to decide if the information should be displayed. If it is TRUE, the information will be displayed while the model is running. Otherwise, the information will not be displayed. By default, it is FALSE.

Details

The major difference between original and modified averaging method is the averaging methods for the case where the minimization and maximization rules are not simultaneously satisfied. For example, for a queried target set there are supersets but not subsets in the training data, the original algorithm will take the prediction from these supersets data using the minimization rule. However, the modified algorithm will further adjust the prediction using the average between such a prediction and 0.

Value

A list containing the following components:

timma	a list contains: the predicted efficacy matrix, prediction error and predicted drug sensitivity
k_sel	the indexes for selected targets

Author(s)

Liye He [e-mail]

Examples

## Not run: 
data(tyner_interaction_binary)
data(tyner_sensitivity)
result<-floating2(tyner_interaction_binary, tyner_sensitivity[,1], sp = 1, max_k = 5)

## End(Not run)

timma documentation built on May 2, 2019, 1:10 p.m.