Firstly, we create some synthetic positive examples with few negative noises to enlarge positive examples set P. Secondly, we extract a representative negative example set RN from unlabeled genes U using SVM iteratively.
As shown by table 2, twoclass has the most serious imbalance and PSoL has more serious imbalance, but our method SPE-RNE, like twoclassbal, doesn't suffer from imbalanced problem at all because we select reasonable quantity of negative examples after enlarging the positive examples set.
In this paper, We propose a novel approach to predicting gene function for genes with few positive examples and unlabeled ones SPE-RNE: creating synthetic examples to enlarge the set of positive examples, extracting representative negative examples from unlabeled examples and training SVM classifier using Grid-search technique.
We enlarge positive example set with different times for different groups and set k = 10, which is shown in Table 7. Particularly, we did not make synthetic positive examples for functional classes with more than 300 annotated genes.
Firstly, positive examples set is enlarged by creating synthetic positive examples.
Presenters earn points For audiexampleenlarged audible resplayer like claps and cheers.
For example, enlarged vestibular aqueducts suggest Pendred syndrome and mutations in SLC26A4.
Examples include enlarged heart primordium and abnormally enlarged ventricular chambers, detached pericardial sac, small forebrain, flat telencephalic vesicle, failure in neural tube closure, and small and irregularly shaped eyes.
ADAM: Enlarged prostate, enlarged prostate, enlarged prostate!
Fung's method enlarges the positive examples set after selecting the reliable negative examples, but, if we use Euclidean distance or cosine distance, a better positive example centroid can not be found due to irregularity of positive examples distribution, which has been validated by our many experiments.
