Given that many machine learning problems in biomedical research do involve positive and unlabeled data instead of negative data, we believe that the performance of machine learning methods for these problems can possibly be further improved by adopting a PU learning approach (Cerulo, et al., 2010; Mordelet et al., 2008), as we have done here for disease gene identification.
Fourth, analysis of data on negative emotional states and sleep quality were based on self-report rather than objective measures.
mxtrmodLRT outputs a data frame of negative log likelihoods, χ statistics, degrees of freedom and P-values for each metabolite.
Raw data, comprised of negative and positive spleens per group, were analyzed by Fisher exact test for determination of probability values (p-value).
According to our staged intervention plan, the resistance data of Gram-negative isolates (E.coli, Klebsiella, Pseudomonas and Acinetobacter) and Gram-positive isolates (Staphylococcus aureus, Coagulase-negative Staphylococci and Enterococci,) were retrospectively analyzed over a one-year period (July 2007 to June 2008).
Imputed genotype data of ACPA-negative RA patients was extracted from the RA meta-analysis in a Japanese population.
There is a positive and a negative control with simulated data; of these, the negative control with simulated data is the experiment reviewer 3 requested.
In short, SVMs initially map input data (in terms of negative and positive class input vectors) into a high dimensional feature space using a kernel function [27] [29].
Then, SVM, Rocchio and Naive Bayes algorithms are used as base classifiers to construct an ensemble classifier, which runs iteratively and exploits margins between positive and negative data to progressively improve the approximation of negative data.
The data of the untreated negative control cells is shown in (c).
